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Senior Applied Data ScientistBelvo · 🇧🇷 São Paulo / 🌎Remote

Belvo

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Senior Applied Data Scientist

🗓️ Posted 2026-10-15 🇧🇷 São Paulo / 🌎Remote Fulltime Hybrid ICT
💰 Salary Not shared 📊 Market 15,000 EUR–25,000 EUR/mo

About the Company

A little bit about us: We are Belvo, an open finance API platform with the bold vision of democratizing access to financial services in Latin America. We enable any financial innovator to access and interpret financial data, as well as initiate payments from their end-users accounts. We're turning the messy complexities of the Latin American financial ecosystem into a modern set of tools to access and interpret data and move money in a seamless and secure way. We're a highly-technical, passionate, and driven team. We are more than 90 people and our team currently represents 20 nationalities. We have offices in São Paulo and Mexico City - while a large portion of us work remotely. We are tackling a very stimulating problem: connecting fintech innovators with legacy financial infrastructure. We strive to go beyond the limits of what is possible today and to do so in an elegant and developer-first way. Since starting our adventure in May 2019, we have raised 71m from the leading VC investors globally. You can read more about our company here https://belvo.com/about/ and about our team and culture here https://belvo.com/careers/. Also, head to our blog https://belvo.com/blog/ for more news about what we're building and how we work https://belvo.com/blog/category/working-belvo/. About the team: - We work in cross-functional, autonomous teams. We follow continuous delivery best practices executed on top of a modern technology stack. - Our products are built for develope

Responsibilities

  • ● Model development and selection: Lead the design of statistical and ML models (classification, regression, ranking, NLP or LLM-based) for problems like risk scoring, fraud detection and transaction categorization. Choose approaches on their merits and defend those choices with data, not habit.
  • ● AI-leveraged delivery: Use agentic coding and research tooling as a core part of how you work: exploring data, generating and testing feature hypotheses, scaffolding pipelines, writing the service around the model. We care about the decisions you ship, not hours at the keyboard, and we expect you to keep compressing the gap between the two.
  • ● Feature engineering with business context: Work with Product and the business teams to figure out which signals in Belvo's financial data actually explain the outcome you're modeling, and translate an ambiguous business question into a well-scoped modeling problem.
  • ● Productionization and ownership: Take models past the notebook. Build or shape the pipelines, APIs and services that run them reliably in production, and own them once they're live: monitoring, drift, retraining, and retirement when the evidence says so.
  • ● Experimentation: Design and run experiments (A/B tests and offline evaluation) to confirm that a model change actually moves the metric it's meant to move.
  • ● Cross-functional partnership: Work closely with Engineering, Data Research, Product, and the rest of the Intelligence Squad to ship end to end, and with stakeholders across the business to keep your models grounded in what actually matters to them.
  • ● Business and customer partnership: Work directly with stakeholders and, when needed, customers. Translate ambiguous business questions into well-scoped problems, and explain trade-offs and impact clearly to non-technical audiences, executives included.
  • ● Staying current: GenAI moves weekly. We expect you to be genuinely plugged in: model releases, papers, and tooling, to hold an opinion on what's real versus hype, and to bring the useful parts back to the squad as a demo, a doc, or a session for our company-wide AI Chapter.
  • ● Framing the problem: Decide what's worth modeling. Push back when a rule, a heuristic, or a better data pipeline solves the problem without a model, and be willing to kill or retire your own model when the evidence says so.
  • ● This position may be for you if
  • ● + years of experience as a Data Scientist, Applied Scientist, or in a similar hands-on modeling role.
  • ● You've owned models end to end in production, from framing the problem to monitoring it live, and you can point to the measurable business impact they had.
  • ● You have a solid grounding in statistics and can explain how your models work at that level, not just how to call them.
  • ● You've built and compared more than one type of model (for example tree-based methods, regression, deep learning) for the same problem and can justify why you picked the one you shipped.
  • ● You're comfortable in Python for data science (pandas, scikit-learn, and at least one of TensorFlow or PyTorch) and in SQL for pulling and shaping your own data.
  • ● You've taken several models you built into production yourself, not just handed it off to someone else to deploy.You http://deploy.You understand engineering fundamentals: testing, version control, CI/CD, and how to write code someone other than you can maintain.
  • ● You've built with LLMs past the API-call level: tool use or agents, retrieval, structured outputs, and shipped at least one to production. You can explain how you knew it was good enough, which means you've built some form of evaluation, not just eyeballed outputs.
  • ● Agentic coding tools are part of your daily workflow, not a novelty. You can walk us through a specific task that used to take you days and now takes hours: how you set it up, what you delegated, and where you still don't trust the output.
  • ● You're proficient in written and spoken English.
  • ● You've worked in fintech, financial risk, or another regulated data environment before.