Senior Applied Data Scientist

A little bit about us

We are the company, 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 [link for members] and about our team and culture here [link for members] Also, head to our blog [link for members] for more news about what we’re building and how we work [link for members]

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 the company's AI enabler team. One part of the squad builds the platforms and patterns that let every team at the company 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 the company'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

• 5+ 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 [link for members] 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.

Amazing if

• You've worked in fintech, financial risk, or another regulated data environment before.

• You have a business or strategy background alongside data science (an MBA, consulting, or a product/strategy role).

• You've worked in a customer-facing data science role, such as a forward-deployed engineer or a solutions/applied team at a data platform company.

• You've worked with MLOps tooling for model versioning, monitoring, and automated retraining.

• You've deployed models on AWS (for example SageMaker or an equivalent managed ML service).

• You've written evals or an LLM-as-judge harness for a non-deterministic system, and have opinions about where that approach breaks.

• You've used LLMs inside the data pipeline itself: labeling, extraction, categorization, and can say where they beat a classical model and where they quietly don't.

Tech stack

• Python is our primary language for data science and modeling (pandas, scikit-learn, and deep learning frameworks as needed).

• SQL against a cloud data warehouse for data access and feature work.Claude deployed company-wide and Claude Code for every engineer. Agentic tooling is part of how we work daily, not a side experiment.

• Cloud infrastructure on AWS, favoring managed services so you can focus on the modeling problem rather than infrastructure plumbing.

• An experimentation platform for A/B testing and offline evaluation.

• We use Datadog for service observability across the company.

• We follow Continuous Integration and Continuous Delivery best practices.

Our tech stack

• We’re building our platform using modern technologies, putting the focus on reliability and long-term maintainability

• We primarily use Python on the backend. We use battle-tested technologies such as Django, and we are heavy users of Python’s asyncio for some parts of our stack

• We use Javascript, Vue.js and Sass on the frontend. We are developing and leveraging our own design system and component library

• We run our infrastructure on top of Amazon Web Services, leveraging managed services. We tend to favor the use of managed services wherever possible, in order to focus on our business problems

• We observe and monitor our services using Datadog

• We follow Continuous Integration and Continuous Delivery best practices

Our process steps

At the company every hire is so important to us, that we share the decision to hire as a team.

• People team chat

• Take-home challenge

• Challenge presentation

• Team fit chat

• Meet the founders

Our perks

🚀 Stock options (we are all owners and this is very important to us)

📈 Annual company bonus linked to company performance

🔆 Flexible working hours

🔝 Remote friendly

🐶 Pet friendly

👩🏻‍⚕️ Health Insurance

🎊 Paid time off on your birthday

💻 Renew your laptop every 3 years

🙋 Training Budget

😎 Team building events

🙀 Bank holidays swap within 30 days

🔋 Fitness/ wellness stipends

🚀 Yearly company offsite

🍒 Fresh fruit every week, all-you-can-drink tea and coffee

🏝 Extra days off accrued when completing 2, 3, 4 years and beyond at the company

🎳 Yearly department offsite