Senior Applied Data Scientist

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  and about our team and culture  here . Also, head to our blog for more news about what we’re building and how we work .

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 developers, by developers. Technological excellence is at the heart of what we do.

We are pragmatic and customer-focused. We strive to find the right set of trade-offs in order to validate our hypothesis as early as possible, iterating on our products based on customer feedback.

We communicate transparently. We do weekly all-hands where we get together to discuss company performance and goals.

While we are global and remote-friendly, we also operate from our vibrant offices in CDMX and Sao Paulo. To accommodate the various time zones in which we are based, we ensure we’re always synced up for at least 3 hours per day.

Also, we are backed by some of the leading investors in Silicon Valley and Latin America, including Founders Fund, Kaszek Ventures, and YCombinator.

The challenge

The Intelligence Squad is Belvo's AI enabler team. One part of the squad builds the platforms and patterns that let every team at Belvo ship AI. This role sits in the other part: turning the financial data and payments we aggregate across Latin America into models that make real decisions, who looks creditworthy, which transaction is fraudulent, what category a payment belongs to, whether an income statement checks out. That data drives real financial outcomes for the people and businesses on the other end, so the bar for rigor is high.

You'll be the data science reference in the squad. We're not hiring someone to execute a modeling backlog someone else wrote. We want someone who decides which problems are worth modeling, defines how we know a model is good enough, and raises the data science bar for everyone they work with. You'll be deep enough in statistics and ML to defend a modeling choice to anyone, whether this is a set of rules to simplify a problem or a complex model that you build. You'll be close enough to the business to turn a commercial problem into a modeling roadmap and a business case. And you'll be a strong enough engineer to take your models from notebook to production and own them there.

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.