Ricardo Vinhas

🌐

As Director of Data Strategy at NielsenIQ, I was also responsible for managing partnerships with data providers across Europe. We developed a segmentation model based on three key axes: (1) the digital maturity of each market — including the prevalence of modern vs. traditional trade and the partners’ ability to connect to our digital ecosystem; (2) strategic alignment with our product and quality standards; and (3) overall market potential. By enriching this model with behavioral and transactional data, we created tailored engagement journeys that significantly increased partner activation and improved data flow efficiency across key markets.

I stepped into the Data Strategy role at NielsenIQ in January 2020, just weeks before theCOVID-19 crisisdisrupted traditional data collection across multiple markets — especially in countries heavily reliant on manual processes and traditional trade. Practically overnight, we faced a data supply crisis that put product delivery at serious risk. In response, I led the creation of a multi-country partner task force, launching urgent global RFPs, onboarding dozens of new vendors, and building a digital-first sourcing network. We adapted methodologies, overhauled operational flows, and redefined our sourcing playbook — all while ensuring zero disruption to client deliveries during a period of extreme uncertainty. What began as a crisis response evolved into a long-term strategic program. By 2024, we had achieved 100% data collection digitalization in Europe and successfully deployed our digital product, MarketTrack, across all countries. This remains one of the achievements I’m most proud of — turning a global crisis into a platform for innovation, resilience, and sustainable transformation.

One of the most impactful partner-led transformations I’ve driven required both courage and conviction. At NielsenIQ, our sales estimation model for retail chains in Europe was based on a traditional process that was highly complex, expensive, and difficult to scale. However, any methodological change in this space could affect trend consistency — a critical concern when clients rely on our data for multimillion-euro marketing and pricing decisions. I initiated and led a competitive RFP process to explore alternative approaches, eventually identifying a partner with an innovativecrowd-sourcing technology. After thorough testing and stakeholder alignment, we rolled out this new model across 10 European markets, replacing the legacy process. The result was a significant improvement in data accuracy and consistency, as well as substantial cost and time savings. It also paved the way for future tech-enabled estimation models. What made this campaign so fruitful wasn’t just the operational success — it was the mindset shift it created across our analytics, product, and commercial teams.

While we didn’t formally call it “ABM” at NielsenIQ, I worked extensively with its core principles — especially in supporting our global premium clients. These accounts received high-touch, personalized engagement models, combining tailored insights, fast-track support, and dedicated solutions aligned with their strategic goals. My role was to collaborate closely with product, data operations, and account teams to ensure that each client’s journey was insight-driven and fully contextualized. We implemented custom workflows, proactive communications, and flexible data sourcing to meet each account’s specific needs — which is the essence of an ABM approach. In parallel, we also developed scalable strategies for SMB clients, focusing on automation, modular deliverables, and simplified onboarding — ensuring we could maintain relevance and value even in lower-touch models. This dual approach allowed us to grow across the client spectrum while optimizing effort and impact. Over the years, I’ve worked across both ends of the Martech spectrum — from enterprise analytics to full-stack digital execution. At NielsenIQ, I used SAS, R, and Python to model consumer behavior, automate reporting pipelines, and optimize insight delivery. I also worked with Salesforce to align account-level strategies and client engagement workflows. These tools supported high-impact campaigns for global clients, where data precision and methodological rigor were critical. In parallel, as co-founder of Baianear, I’ve led a full marketing operation — from the ground up. We built a modern, highly responsive website, structured multi-channel presence (Instagram, Google, TikTok), and launched campaigns through Meta Ads, Google Analytics, and SEO. We also expanded brand engagement through digital out-of-home (DOOH) media, original branded music across streaming platforms, and influencer collaborations. One standout campaign involved a hyper-targeted Meta strategy during our off-peak season. By combining behavioral retargeting, geo-segmentation, and optimized landing pages, we tripled direct reservations in two weeks — fully tracked via GA4 and Meta tools.This hands-on experience with a complete marketing ecosystem — from awareness to conversion and retention — gives me a strong foundation to evaluate and lead Martech strategies at any scale.

I believe ROI in partner marketing needs to be grounded in real business impact, not just theoretical attribution models. My approach combines practical financial metrics with engagement indicators — always tailored to the nature of the program. In a corporate context like NielsenIQ, I’ve measured ROI using a mix of: ·Pipeline contribution and deal acceleration for co-marketing initiatives; ·Adoption rates and renewal impact for enablement programs; ·And partner NPS or satisfaction scores when the goal was ecosystem strength.That said, as an entrepreneur with Baianear, I’ve also learned the discipline of tracking ROI line by line in the P&L sheet — every euro spent on partner activations must be tied to a tangible uplift, whether in revenue, foot traffic, or customer retention. I’ve built dashboards that link ad spend to reservation behavior, used control groups to validate uplift, and constantly reconciled marketing costs with actual business performance. So whether in a corporate or entrepreneurial setting, I always ask: What’s the actual movement in behavior or revenue? That mindset helps cut through vanity metrics and focus on true return. One of the most transformative experiences I’ve had with product messaging was during the global rollout of our Zero-Touch Validation solution at NielsenIQ. Originally seen as a back-end automation feature, I led a reframing initiative to position it as an innovation enabler — a strategic capability that freed up local teams to focus on insight generation instead of manual checks. This repositioning was embedded into internal communications, client narratives, and product documentation, leading to a 5x increase in internal adoption and rollout across 20+ countries. It became a flagship example of automation with purpose. Similarly, with Trip Projection, I led efforts to better articulate the value of integrating our retailer sales data with consumer panel behavior — a unique analytical fusion that enabled granular modeling of shopper missions and store-level dynamics. By crafting the right messaging and supporting narratives, we elevated its status from “advanced analytics” to indispensable planning tool, especially in retail and trade marketing contexts. More recently, with the development of MarketTrack, I helped shape its positioning around real-time, on-demand, in-depth market insights, tailored for decision-makers navigating volatile environments. By focusing on immediacy, control, and accessibility, we shifted the conversation from “tracking data” to “operational intelligence” — making it How do you balance working in-house and working with agencies? I see the relationship between in-house teams and agencies as a strategic balance between control and perspective. Internal teams bring context, consistency, and long-term ownership — essential for protecting brand and product alignment. Agencies, on the other hand, offer a mirror view: they challenge assumptions, introduce fresh ideas, and often help uncover opportunities we may not see from the inside. This mindset started early in my career. While working at Instituto Painel Brasil, I helped design political campaigns where messaging was initially shaped by the candidate’s core team. But after conducting focus groups and community discussions, we often emerged with entirely new ways of reaching and resonating with people — more human, more relevant, and far more effective. That experience taught me to treat external perspectives not as interference, but as strategic intelligence. Today, I apply that same principle: I keep strategic direction in-house, but actively involve agencies and partners in co-creating value — through clear briefs, shared goals, and continuous feedback. When this relationship is well-structured, the agency becomes not just a vendor, but a catalyst for innovation.

I believe creativity and delivery aren’t opposites — they’re two sides of the same coin. The key is to create a structured environment that protects space for exploration while keeping teams accountable to clear outcomes and timelines. I use agile rituals (like sprint planning, retros, and creative stand-ups) to create rhythm, visibility, and focus. At the same time, I actively protect “white space” in the roadmap — moments where the team can test, iterate, or co-create ideas without immediate pressure to deliver. One development that has fascinated me recently is the use of AI tools to bridge that gap. I’ve used generative AI (e.g., image generation, content ideation, audio tools) to expand creative boundaries, while also dramatically accelerating production. In my current entrepreneurial work, this has helped us move from concept to execution in hours, not weeks, without compromising quality. Ultimately, I see my role as creating an environment where creativity can flow within a purposeful frame, supported by the right tools and mindset — so that innovation and execution reinforce, not contradict, each other.

That’s an excellent question — and one I truly appreciate. To me, leading a Marketing team is both a discipline and a craft — a balance of structure and soul. I see myself equipped with brushes (tools) soaked in data and insight, ready to shape ideas with precision while leaving room for intuition and creative flow. The science lies in process: setting clear goals, tracking KPIs, and building repeatable systems that enable scale. The art emerges in how we bring purpose, emotion, and narrative to those structures — how we tune into people, sense opportunity in ambiguity, and give space for original thinking to flourish. Ultimately, my goal as a leader is to polish the edges of values, align purpose with performance, and create the emotional and operational space for teams to excel and thrive. It’s in this duality — metrics and meaning — that the real magic of marketing leadership lives.

My ideal Martech stack is built around one core principle:delivering a 360° omnichannel view of the consumer— actionable, measurable, and fast. must serve both sides of the equation: ·Internally, it needs to be results-driven, client-oriented, and insightful — enabling precise segmentation, predictive modeling, and real-time optimization. ·Externally, it must support experiences that are inspirational, relevant, and frictionless for the end user.From a technical standpoint, I look for a modular architecture with: ·A Customer Data Platform (CDP) to unify behavioral and transactional data ·A CRM integrated with Marketing Automation (e.g., Salesforce + Pardot or HubSpot); ·A powerful BI & Visualization layer (e.g., Tableau, Looker, Power BI); ·Tools for personalization, A/B testing and journey orchestration (like Optimizely or Adobe Target); ·And flexible data pipelines and cloud infra (BigQuery, Python, DBT, etc.) for modeling and enrichment. Above all, it must be scalable and connected — so that insights flow as fast as the market moves, and decisions are grounded in both data and empathy.

I was one of the key architects and global change agents behind what became NielsenIQ’s Consumer Information Platform (CIP) — our equivalent of a Customer Data Platform for the Consumer Panel (CPS). In 2009, I helped found the CPS HUB team, with a bold mission: to replace a fragmented landscape of local, siloed, and often inconsistent platforms with a unified, modern, and harmonized global system. The challenges were significant — we faced divergent technologies, methodologies, data structures, and knowledge silos that led to conflicting outputs and client dissatisfaction.Over the next 10 years, I led the global rollout — traveling extensively to evangelize the vision, align local teams, and guide the adoption of new processes. We built a platform that was not just technically integrated, but also methodologically consistent and fully documented, enabling global comparability and faster insight delivery. Today, this platform operates in 26 countries, supporting thousands of users and millions of datapoints with a single source of truth. I take pride in having helped turn a chaotic legacy into a resilient, intelligent, and client-ready infrastructure — a true CDP in spirit, architecture, and impact.

During the early development of the Consumer Information Platform (CIP) at NielsenIQ, I encountered a major challenge in Germany — one of our most strategic and high-performing markets. The German team had been using their own data processing platform for over 20 years. It was mature, integrated, and beloved by clients. When I arrived to promote the adoption of our new global platform, I quickly realized their resistance wasn't emotional — it was based on pride in a system that truly worked well. That night, back at the hotel, I went through the documentation they shared — only to discover it was written entirely in German, in a now-obsolete programming language, by a developer who had long since left the company. No one could maintain or evolve the system anymore. The next day, I changed the tone of the conversation. I acknowledged the platform’s merits — it was indeed brilliant — but I showed the risks: lack of scalability, maintainability, and future alignment. More importantly, I explained the value of a harmonized global methodology, where clients could interpret insights consistently across markets. Through diplomacy, trust-building, and a co-creation mindset, we negotiated a path forward: we incorporated the best functionalities of their local platform into the new global backbone. What started as a conflict became a breakthrough — and Germany became a core contributor to what is now a globally adopted, integrated platform running in 26 countries. This experience taught me that technical integration is never just about systems — it’s about people, culture, and long-term vision.

Without false modesty, the data project I’m most proud of is the one that changed my life. I was a 10-year-old boy in Bahia, in the northeast of Brazil, when I first discovered — entirely self-taught — how to fix old computers. That experience opened the door to a world of programming, systems, and, eventually, data. Growing up in a humble environment, this fascination became my path forward. I graduated in Statistics from a top public university and, at 23, left my home and family to pursue a dream in Portugal. That decision — both technical and personal — led me to a global career. Over 18 years, I visited more than 50 countries, participated in international conferences, and became part of the global leadership team of a major corporation. Through data, I was able to transform not only my professional path, but also my family's future. And despite all this, I feel more curious and energized than ever — especially now, witnessing the rise of Artificial Intelligence. It’s that same childhood fascination with systems and logic that now drives me to explore AI, step out of my comfort zone, and once again take flight toward new challenges. This is why data isn’t just what I work with — it’s what shaped me.

Over the years, I’ve worked with several attribution models — from first-touch and last-touch to linear, time-decay, and data-driven approaches. Each serves a purpose depending on the organization’s data maturity and marketing mix. One key insight I’ve developed is the importance of recognizing the Zero Moment of Truth (ZMOT) — the phase where consumers explore, compare, and evaluate options before any direct interaction. Many attribution models overlook this stage, which often involves organic search, reviews, content, and social proof. Ignoring it leads to undervaluing high-impact channels like SEO or top-of-funnel content. That’s why I favor a hybrid attribution model, with time-decay as the baseline (to reward nurturing across the funnel) and customized logic to weigh early-influence channels that drive ZMOT engagement. To implement this in an organization, I’d start by: 1. Auditing data completeness across touchpoints (including organic and social); 2. Mapping the full journey, including pre-click signals; 3. Aligning with key stakeholders on goals and channel roles; 4. Prototyping models and validating against known business cases; 5. Iterating with ongoing feedback to fine-tune. Attribution should serve decision-making — and that includes shining a light on moments that happen before the lead even enters the CRM.

I believe ROI in marketing needs to be grounded in real business impact, not just theoretical models. My approach is both structured and pragmatic. In corporate settings like NielsenIQ, I’ve used a combination of: -Pipeline contribution and deal velocity; -Revenue influenced vs. generated, especially in multi-touch environments; -Control groups and incrementality testing to validate lift; -And engagement indicators (e.g., content consumption, conversion intent) that tie into pipeline movement. That said, as a business owner at Baianear, I’ve learned to track ROI with full accountability — line by line in the P&L sheet. Every euro invested in marketing is tied to uplift in reservations, footfall, retention, or reputation. We've built dashboards that track ROI per campaign, integrating Meta Ads, Google Analytics, and booking behavior to optimize in near real time. One key insight is the importance of capturing the Zero Moment of Truth (ZMOT) — those early touchpoints (like reviews, search, social engagement) that often precede direct conversion but are essential to influence it. Many organizations overlook this when calculating ROI, which leads to underinvestment in high-impact early-stage channels.So whether in a large organization or an agile business, I always ask: What behavior changed? What outcome did we drive? ROI isn’t just about attribution — it’s about decision clarity and long-term value creation.

I treat agencies as strategic partners — especially in performance marketing, where agility, media-buying scale, and cross-platform expertise are key. In-house teams stay focused on strategy, brand voice, and data governance, while agencies execute with speed and precision. To make this work, I ensure clear SLAs, shared dashboards, and weekly syncs. We also run joint retrospectives to optimize performance. My background in both corporate and entrepreneurial settings helps me create accountable partnerships that deliver not just impressions — but measurable outcomes tied to business goals.

Absolutely — lead scoring and nurture streams are not only still relevant, but increasingly critical in today’s landscape. What’s changing is how we implement them. With the rise of AI-powered agents, we now have the ability to manage these streams with far greater precision, context sensitivity, and timing. Instead of static scoring models or rigid workflows, we can design adaptive journeys, where the system learns from behavior and intent in real time — and responds accordingly. In practical terms, this means moving from simplistic rule-based scoring to models enriched by machine learning, combining CRM data, web behavior, campaign engagement, and even qualitative signals. Nurture streams become more conversational, personalized, and truly responsive. In my view, this evolution will significantly enhance conversion and deepen client relationships — especially in B2B environments with longer decision cycles. It’s no longer about qualifying leads — it’s about guiding them with intelligence and empathy.

In my experience, project success and stakeholder management are deeply interconnected. You can’t deliver impact without alignment — and you can’t align effectively without structure. For me, the key steps include: 1.Clear definition of success criteria, jointly agreed with stakeholders — not just output, but business outcome; 2.Establishment and tracking of a robust KPI system, including qualitative and quantitative metrics; 3.Regular toll-gates or milestones to ensure alignment with corporate direction, financial planning, and customer expectations; 4.Ongoing engagement with stakeholders, not just to report progress, but to involve them in critical decision points — which builds trust and shared ownership; 5.A strategically structured go-to-market plan, where the customer’s voice is integrated not just at the end, but throughout the process — ensuring relevance, usability, and value. Ultimately, I believe high-performing teams are those that get things done through collaboration, clarity, and continuous validation of purpose.

I’ve always believed that experimentation is the engine of innovation — and to make that engine run, three things are essential: 1.A culture of error tolerance. As a statistician, I learned early on to treat error not as failure, but as feedback. We must normalize failure as part of the learning process, and create a psychologically safe environment where people feel empowered to test, challenge, and iterate. Breakthroughs rarely come without trial and error. 2.Trust among team members. Experimentation requires vulnerability — and that only exists where there is trust. I actively nurture horizontal relationships, celebrate shared wins (and learnings from losses), and create rituals where people can co-create, reflect, and grow together. 3.Systems for controlled experimentation. Culture alone isn’t enough — we need structure. I implement frameworks and platforms that support experimentation at scale: A/B testing tools, test-and-learn sandboxes, retrospective reviews, and fast feedback loops. We measure impact quickly, course-correct often, and share learnings transparently. In short, I lead by example — I celebrate curiosity, tolerate failure, and always ask: what did we learn that we wouldn’t have learned without trying?

For me, effective data storytelling follows a structure as natural as telling stories to children: it must be clear, visual, purposeful — and it must make sense from beginning to end. Over time, I’ve come to rely on a simple framework: (1) Context, (2) Drive, (3) Findings. 1.Context – Start with “why this matters.” Set the scene, define the business question, and ensure the audience is grounded in the reality that gives meaning to the data. 2.Drive – This is the tension, the journey. What patterns emerged? What changed? What was surprising? Here, I guide the audience through the analysis, showing not just numbers, but relationships, anomalies, and stories behind the behavior. 3.Findings – Finally, land with clarity. What does it all mean? What should we do next? I always close with an insight or a decision point — a call to action that turns storytelling into strategy. Whether I’m speaking to executives, product managers, or clients, I adapt the language and visualization style to the audience. But the goal is always the same: transform data into movement — action, alignment, and shared understanding.

What draws me to Customer Success in the technology sector is the idea that it’s not a destination — it’s a journey. Success isn’t a static KPI; it’s the continuous alignment between what the customer needs, what the technology can deliver, and how that evolves over time. I’ve always been passionate about understanding consumer behavior and satisfaction dynamics. In fact, during my Master’s thesis at Nova IMS, I developed the ISLT – Tourist Satisfaction and Loyalty Index. It was a data-driven model designed to uncover the behavioral factors that most influence loyalty. That project showed me how powerful it is to turn qualitative experiences into measurable, actionable insights. In Customer Success, I see that same challenge — and opportunity. It’s about using data, empathy, and technology to guide the customer through transformation. Whether through predictive analytics, health scoring models, or personalized journeys, I’m especially excited by how technology can operationalize care at scale, and how data can reveal not only where the client is, but where they’re heading. Ultimately, what excites me most is building relationships that are proactive, insightful, and strategic — and contributing to organizations where Customer Success is not just a function, but a mindset.

In the SaaS world, Customer Success is not just about solving problems — it’s about ensuring that the client continuously extracts value from the solution, in a way that’s measurable, evolving, and aligned with their business goals. Best practices in this space often revolve around three pillars: 1. Adoption – Ensuring users are not only onboarded correctly, but are fully engaged with key features that drive value. This requires monitoring usage data, running enablement programs, and sometimes redesigning journeys to remove friction. 2. Retention is not just about preventing churn — it’s about enabling sustainable usage. We need to ensure that the product becomes embedded in the customer’s operations, workflows, or habits. When usage is consistent and value is clear, retention becomes a natural consequence. 3. Expansion & Lifetime Value – Turning trust into growth. When the client sees tangible results, they’re more likely to expand usage, adopt new modules, or become advocates. The role of CS here is to anticipate needs and surface the right opportunities at the right time. From my background in market insights and behavioral modeling, I believe one of the most powerful levers for Customer Success is understanding what success looks like from the client’s perspective — and continuously adapting the tech experience to meet that. Data, empathy, and strategic alignment are the real drivers of long-term value in SaaS.

In enterprise SaaS, Customer Success results are best measured by how effectively we drive long-term value realization for both the client and the provider. That value manifests in different layers: 1.Net Revenue Retention (NRR): The core financial indicator — capturing retention and expansion in a single metric. 2.Product Adoption & Health Scores: Monitoring usage intensity, feature engagement, support load, and sentiment signals — often feeding into predictive churn or expansion models. 3.Time-to-Value (TTV): A key operational indicator, especially critical in complex onboarding environments. 4.Advocacy & Strategic Alignment: Case study participation, co-innovation initiatives, and cross-functional collaboration. From a statistical perspective, I believe in modeling these as latent constructs, where Customer Success is not measured by any single metric, but rather as a composite indicator emerging from structured relationships among these variables. In fact, I’ve applied similar frameworks in past work — including my thesis on satisfaction modeling — where we used structural equation models to uncover behavioral drivers of loyalty and engagement. While I’ve not held a CS-specific title, I’ve worked across functions where renewal probability, customer satisfaction, and insight adoption were key success metrics — and always approached them with analytical rigor, client empathy, and a long-term mindset.

If I had to choose one customer that represents the essence of Customer Success for me, it wouldn’t be a global corporation — it would beDona Maria, a 90-year-old lady who visits our restaurant, Baianear, once a month. She comes accompanied by her family from the nursing home where she lives. It’s the only time in the month she leaves the institution — and she always chooses to come to us. She arrives dressed in white, elegantly prepared, and with a smile that lights up the room. She eats very little, but that’s not the point. She comes for the experience, the warmth, the human connection. She often brings family members who might prefer high-end restaurants. Yet, she insists on coming to Baianear. She once whispered to me, “At my age, I’ve learned that true value isn’t in the price of things, but in the way we’re made to feel — and you all always make me feel at home.” That, to me, is what Customer Success truly means. It’s not just about adoption, retention, or expansion. It’s about creating moments that matter. Whether I’m working with enterprise clients, global data platforms, or a beloved guest like Dona Maria — the goal is the same: to listen, to care, and to build something that genuinely enriches someone’s life. She reminds me that sometimes, the greatest loyalty comes not from what we deliver — but from how we make people feel while delivering it.

The innovation I most want to bring to Customer Success is a truly human touch — empowered, not replaced, by AI. I imagine a future where technology doesn’t just automate responses or scale outreach, but acts as a magnificent lens that reveals the rhythm behind each customer’s journey. Too often, Customer Success gets caught between rules and chaos — either over-structured with rigid playbooks, or overwhelmed by complexity. What I envision is a harmonized and synchronized dance between art and science: ·The art: listening deeply, adapting intuitively, recognizing emotional signals that create trust. ·The science: using predictive analytics, AI-powered agents, and dynamic scoring models to scale care, not just operations. By blending these worlds, we can shift from reactive support to orchestrated partnership, where each customer feels understood, guided, and genuinely valued. In practical terms, this means designing systems that are not only smart, but empathetic by design — where every data point is a clue, and every interaction is an opportunity to build loyalty through meaning. That’s the future I want to help build: one where technology reveals the humanity at the center of every customer story.

For me, engagement is about presence. I like to stay close — not just with formal meetings or dashboards, but with real conversations that create connection. I listen attentively, try to read between the lines, anticipate needs, and truly make myself available. With strategic clients, I keep a steady rhythm of interaction — but always grounded in what’s meaningful to them. Sometimes it’s a new insight, a recommendation, a quick check-in, or even a thoughtful silence when that’s what they need. For smaller clients, I use automation to scale communication while keeping it human — milestone-based messages, timely nudges, and relevant content.I believe in nurturing the relationship over time, not just around deliverables or contract milestones. Clients need to feel that I’m genuinely on their side — and that every interaction adds something of value, however small. In the end, that’s what builds trust. And that’s what makes this work meaningful to me.

I believe the best approach to working with Account Management is to build a relationship grounded in transparency, shared goals, and actionable insight. I bring a data-informed and outcome-driven mindset to the table — not just reporting what’s happening, but offering context, trends, and concrete next steps that help account managers turn information into value. I’m proactive in flagging risks, surfacing opportunities, and always focusing on what moves the client relationship forward. At the same time, I see account managers as true partners. They hold the voice of the client, and I respect their role in shaping strategy and relationship dynamics. My job is to give them the clarity and confidence they need to act — and to help us deliver not just services, but strategic impact. In short, it’s about rhythm, alignment, and trust. When those are in place, the collaboration becomes fluid — and the client feels the strength of a unified team. Since an early age, technology has been more than a skill — it's been a form of expression. By 10, I was already fixing computers and programming for fun. By 12 or 13, I was deep into open-source systems like Linux and FreeBSD, using salvaged machines to build IRC servers, deploying eggdrop bots, and even coding my own chatbots to help users troubleshoot issues automatically. That hands-on, exploratory spirit shaped my technical foundation and mindset. Today, what excites me in the enterprise market is the opportunity to apply that same curiosity and system thinking to solve complex, large-scale problems. I’m especially drawn to how open technologies can provide scalability, flexibility, and shared innovation, while also enabling secure, data-driven transformation. Whether through cloud architecture, automation, or AI, I see enterprise tech as a powerful stage where modular ideas turn into real-world impact. Marketing open source to enterprises requires a shift in narrative — from features to trust. The value is in transparency, flexibility, and community validation — but enterprises also need to know they’re getting scalability, support, and security. The messaging must combine the boldness of innovation with the reassurance of reliability. I see an opportunity to elevate open source by showing how it empowers autonomy while still meeting enterprise-grade expectations.

In recent years, I’ve been deeply involved with emerging technologies — not just out of curiosity, but as tools to solve real problems and expand creative potential, especially within my entrepreneurial project, Baianear. I’ve worked extensively with generative AI, particularly diffusion models like Stable Diffusion and ComfyUI, where I design custom workflows for image generation, style transfer, and character creation. I've also explored LLMs (Large Language Models) to automate content generation, enhance user interaction, and support internal operations through intelligent assistants. From a development perspective, I’ve built web-based systems using PHP, JavaScript, and Python, including real-time interfaces that interact directly with local AI models via APIs. These setups allow users to generate visuals or dynamic content directly from the browser, integrated into lightweight infrastructure optimized for speed and control. I also enjoy working at the systems level — scripting automations, customizing nodes, and managing GPU environments to run advanced AI workflows locally. Rather than depending solely on commercial SaaS platforms, I often build tailored, on-premise solutions that combine flexibility, performance, and privacy — reflecting my roots in open-source development and early experimentation with Linux. This hands-on, cross-disciplinary engagement keeps me close to innovation — not just following trends, but actively shaping how new technologies can be applied, refined, and scaled to deliver meaningful outcomes.

Empowering, structured, and outcome-oriented. I focus on creating clarity of purpose, autonomy, and a rhythm of delivery. As teams grow, I introduce squad structures, layered coaching and stronger rituals without losing the human connection.

I’ve led agile squads in data, analytics and product enablement. Comfortable with Scrum and Kanban, I use sprint cycles, retrospectives and backlog planning to drive pace and visibility.

I believe team success must be measured on three complementary levels: ·Delivery – Are we achieving what we committed to, with quality and consistency? I track roadmap milestones, operational KPIs, and client-facing outcomes. ·Engagement – Are people growing, motivated, and proud of their work? At NielsenIQ, we partnered with Gallup for global engagement assessments, and my team consistently ranked in the top quartile across key indicators — something I take great pride in. ·Collaboration – Are we building trust and working well across functions? I look at cross-team feedback, stakeholder satisfaction, and team rituals as health signals. To me, true success happens when we deliver impact without burning out, when people feel seen and supported, and when the team becomes a place of growth, not just execution.

Daily: Stand-ups; Weekly: Team syncs and 1:1s; Monthly: Retrospectives, learning sessions; Quarterly: OKR review, strategic planning, career check-ins.

For me, career development starts not with skills, but with values. I believe the first step is helping entry-level employees understand and internalize the culture and purpose of the organization — how we work, why we do what we do, and how their role fits into a bigger mission. ·From there, I focus on building a strong foundation in three dimensions: ·Critical thinking and structured problem-solving – regardless of the role, these skills build confidence and autonomy. ·Communication and collaboration – helping them articulate ideas, ask better questions, and engage meaningfully with peers and stakeholders. ·Technical fluency – which I tailor to the specific function, whether it’s working with data, mastering tooling, or navigating platforms and codebases. I also encourage cross-functional exposure, mentorship relationships, and early ownership of small but meaningful projects. The goal is to develop not just professionals — but resilient, value-aligned contributors who are curious, committed, and capable of evolving with the company.

I tend to keep my approach simple and grounded in clarity. I don’t rely heavily on complex tools — our team calendar, shared documents, and alignment meetings are usually enough when priorities are well understood. I prioritise tasks based on a few key criteria: ·Impact and relevance of the project to our strategic goals; ·Urgency and complexity of specific issues or roadblocks; ·Stakeholder expectations and visibility — especially when updates or decisions are time-sensitive; ·And the overall value of unblocking others, which often multiplies progress across the team. Throughout the week, I reassess constantly: What’s moving? What’s stuck? What requires my direct involvement vs. delegation? I’m also careful to protect blocks of uninterrupted time for deep work — especially when dealing with analysis or strategic thinking. At the end of the day, for me it’s not about managing time — it’s about managing energy, attention, and alignment.

Yes, I consistently ranked among the top students in mathematics, often tutoring others. I achieved top scores in final exams and was admitted into a top-tier university based on strong math and logic performance.

I performed strongly in Portuguese, with solid grades and frequent selection for school writing competitions. While I wouldn’t claim to have been the very top student in the subject, I was certainly considered among the upper tier. Portuguese had a special place in my life — not just academically, but personally. My mother is a Portuguese teacher, so the subject carried an added layer of importance and respect at home. Language, for me, was never just about grammar or composition — it was about expression, clarity, and connection. That early foundation has stayed with me throughout my career, especially in roles where communication and narrative are key. I graduated with a final average above 8.5/10. I passed the national university entrance exam (Vestibular) on my first attempt and was admitted to the Federal University of Bahia (UFBA) — one of the most prestigious public universities in Brazil, known for its academic rigor and highly competitive selection process. At just 17 years old, I entered the Bachelor’s program in Statistics, which is considered one of the most theoretical and demanding degrees at the university. The program required strong foundations in mathematics, logic, and probability — and it played a major role in shaping my analytical mindset early on.

Yes. Between top marks in high school, early university admission, and a final average of 8-9/10 at both undergraduate and graduate levels — combined with scholarship offers and academic invitations — I confidently place myself in the top 1% of my cohort.

I was a very active and well-rounded student — both inside and outside the classroom. I had a strong academic focus, especially in math and science, but what really defined me was my energy, curiosity, and connection with people. Outside of class, like many Brazilians, I was passionate about football. I played every day and competed in junior league teams — I was quite talented and dreamed of going far with it. At the same time, I was always involved in school life: a class leader, someone who maintained great relationships with both teachers and classmates, and who was present in all the events. I danced in school festivals, acted in short films we produced as a class, and loved being part of any collaborative creative effort. I think my peers would remember me as a natural connector — confident, playful, committed, and full of initiative.

I chose to study Statistics at the Federal University of Bahia (UFBA) — one of Brazil’s most respected public universities, known for its academic excellence and rigorous, merit-based admission. At the time, I was torn between Statistics and Computer Science. I had a natural affinity for both, and coming from a technical background, both felt like viable paths. But I had a close family influence: my aunt had studied Statistics, and more importantly, it was my mother’s intuition that guided me. She said, “This is the field where your mind and your curiosity will grow the most.” And looking back, she was absolutely right — it was a decision that shaped my entire trajectory. Later, I pursued a Master’s degree and postgraduate studies at Nova IMS in Lisbon, drawn by its European leadership in data science, analytics, and digital transformation. These programs gave me an international perspective and the strategic grounding to complement my technical roots. I was also selected for the PhD program at Nova IMS, which I began with great enthusiasm. However, after the first year, I was offered a significant international opportunity in my professional career, and I made the conscious choice to prioritize that path at the time — a decision that allowed me to grow exponentially in global leadership and applied innovation.

My final average was 17/20 — equivalent to "Distinction" — at both undergraduate and graduate levels. This reflects a high level of discipline, analytical skill, and consistent performance across advanced quantitative subjects.

One of the most exceptional achievements in my academic journey was not just what I learned — but what I overcame. At 23, I left Brazil alone, with very limited financial resources, to pursue a Master’s degree in Portugal. I had been invited to join a pioneering program at Nova IMS based on merit — an opportunity I couldn’t afford to waste, even though I faced enormous challenges. The course was taught entirely by international professors in English, and at the time, I didn’t speak a word of the language. I taught myself English on the spot — watching videos, reading materials, and pushing myself to present in front of the class. Despite the language barrier and the cultural adaptation, I was consistently recognized as one of the top students in the program, graduating with distinction and praise from the faculty. My Master’s thesis — the ISLT: Tourist Satisfaction and Loyalty Index — became a reference within the school for its methodological rigor and applied value. I also created peer-led study groups to help fellow students succeed, reinforcing my belief that knowledge only grows when it’s shared. That experience was more than academic — it was a personal transformation, and a milestone that proved to me that with purpose, grit, and humility, it’s possible to turn any disadvantage into strength.

Throughout my academic journey, I’ve always looked for ways to connect knowledge with real-world impact — and that often meant stepping into leadership roles beyond the classroom. One of the most meaningful initiatives I led was during my time in Portugal, when I volunteered with the Jesuit Refugee Service (JRS) during the height of the humanitarian crisis that brought thousands of displaced people to Europe. I proposed and coordinated a project called “Technology Through Football”, aimed at fostering integration and rebuilding community among refugees from diverse nationalities and backgrounds. We have been anble to mobilize through personal mobilization and networking over 20 donated laptops from socially conscious companies, as well as sports equipment from the Portuguese Olympic Committee. I designed the program so that participants could first connect through football — a common language that helped bridge cultural and linguistic divides — and then transition to learning sessions where they built CVs, practiced Portuguese, communicated with missing family members across Europe, and gained basic digital literacy. The project wasn’t just about teaching skills — it was about restoring dignity, connection, and possibility for people who had lost everything. Leading it taught me more about inclusion, leadership, and resilience than any formal class ever could.

TrademarkIQ is an enterprise-grade trademark research and clearance platform I founded to transform how Brazilian companies make brand strategy decisions. The core problem is simple: trademark searching and risk assessment in Brazil has traditionally been time-consuming, fragmented, and inaccessible to many businesses. Companies had to navigate INPI (Brazilian Patent Office) databases manually, rely on expensive legal consultants, or use outdated methodologies — all of which delayed go-to-market timelines and increased IP risk. TrademarkIQ combines three core capabilities: (1) intelligent data integration with INPI and market databases, (2) predictive similarity algorithms that identify colliding marks before filing, and (3) automated risk scoring that quantifies clearance confidence. This allows mid-market and enterprise clients to make trademark decisions in hours instead of weeks, with significantly reduced legal risk and cost. We serve 50+ corporate clients across various sectors, with recurring revenue models through API access, batch processing, and strategic advisory partnerships.

Busca de Marcas is the democratized, SaaS-first counterpart to TrademarkIQ's enterprise positioning. While TrademarkIQ serves large organizations with complex multi-brand portfolios, Busca de Marcas empowers SMEs, designers, startups, and independent entrepreneurs to access the same quality of trademark intelligence at an affordable, self-service price point. It's a freemium marketplace where users can conduct instant prior art searches, receive automated clearance recommendations, and access market positioning reports — all without needing a legal team or significant budget. Busca de Marcas has attracted 5,000+ active users and operates on a freemium-to-paid model, generating revenue through premium searches, detailed reports, and market intelligence subscriptions. The strategic logic is clear: TrademarkIQ captures enterprise revenue through high-ticket advisory and API partnerships, while Busca de Marcas captures volume and ecosystem network effects in the SME segment. Together, they represent a complete vertical in Brazilian brand intelligence — from the C-suite to the solopreneur.

The strategy is fundamentally different for each product, reflecting their distinct market segments. For TrademarkIQ: direct B2B sales to corporate legal departments, procurement teams, and IP management offices. We've positioned the product as a risk mitigation tool and strategic accelerator — not just a search engine. Our sales narrative emphasizes velocity (hours vs. weeks), confidence (quantified risk scoring), and ROI through reduced legal exposure and faster time-to-market. We've built partnerships with IP law firms and corporate consultants who refer clients, creating a channel that leverages existing trust. For Busca de Marcas: product-led growth with a strong focus on organic discovery through SEO, community engagement, and content marketing targeting Brazilian entrepreneurs and designers. We've invested in educational content around trademark strategy, brand protection, and market research — positioning Busca de Marcas as the trusted knowledge hub, not just a tool. The freemium model acts as the primary acquisition funnel, converting users into paid subscribers as their brand portfolios grow. Both products are anchored in the same data infrastructure and IP expertise, creating natural upsell pathways and network effects — users who start with Busca de Marcas can graduate to TrademarkIQ enterprise services as their businesses scale.

The connection is direct and foundational. My 18+ years in data strategy at companies like NielsenIQ taught me how to build scalable, data-driven platforms that deliver measurable client value. At TrademarkIQ and Busca de Marcas, I've applied this experience across three dimensions: (1) Data Architecture: Rather than relying solely on INPI's official databases, we've built a unified data layer that integrates multiple sources — INPI filings, domain registries, market intelligence, competitor portfolios — creating a richer signal for similarity detection and risk assessment. (2) Statistical Modeling: The core similarity algorithms and risk-scoring engines use statistical and machine learning approaches inspired by my work in sampling, calibration, and weighting methodologies. We're not just matching character strings — we're modeling semantic and phonetic similarity in Portuguese, and scoring probability of objection based on historical data and class-specific patterns. (3) Operational Excellence: From NielsenIQ's operations background, I've built governance models around data quality, vendor management, and delivery reliability. Both products maintain strict SLAs and audit trails — critical for enterprise clients and regulators. The venture isn't just about brand intelligence; it's about applying rigorous analytical thinking to a traditionally lawyer-driven market, and proving that technology + data can reduce friction, cost, and risk.

The vision is ambitious but grounded: establish TrademarkIQ and Busca de Marcas as the dominant, trusted infrastructure for brand intelligence in Brazil — and eventually, in Latin America. For TrademarkIQ, we're expanding from trademark clearance into a full brand intelligence platform. This includes trademark portfolio management, monitoring for infringement and conflicts, trademark renewal automation, and strategic advisory on brand architecture across multi-market expansions. We're also exploring vertical expansions — moving into adjacent IP areas like domain management and design patent clearance. For Busca de Marcas, the goal is to become the default first stop for any Brazilian entrepreneur, designer, or SME thinking about brand protection. This involves deepening our content library, building community features (peer reviews, case studies, brand strategy guides), and integrating with broader business tools that SMEs already use. Long-term, we're building a network effect: as more SMEs use Busca de Marcas, the aggregated data creates better insights, which increases platform value for both SMEs and enterprise clients on TrademarkIQ. Within 3-5 years, I envision both products as category leaders in Brazil, with sustainable unit economics, strong retention, and the foundation to expand across Latin America. The ultimate mission: make brand intelligence accessible, affordable, and intelligent for businesses of all sizes — reducing the time and cost of brand strategy, and enabling faster, safer growth in one of the world's most dynamic markets.