Forward Deployed Engineer

We're looking for a Forward Deployed Engineer with a data science background and experience in real-world enterprise decision-making. You'll work directly with clients to deploy AI solutions that transform how they make high-stakes decisions: who to lend to, who to acquire, who to trust. This is a role for someone who understands a customer's business problem as well as they understand APIs, and who can translate an operator's pain into a working prototype.

• Work embedded with clients to deeply understand their decision workflows, data infrastructure, and operations — identify the real pain points and shape them into actionable use cases for Avra's platform.

• Build and deploy AI-powered solutions across Avra's core applications: credit and risk, customer acquisition, and fraud.

• Assess the quality and accuracy of customer data alongside them, ensuring Avra's Data Platform team is set up to consume the right data in the right way.

• Work hand-in-hand with a Deployment Strategist who turns your technical insight into the business solution the customer needs, at speed. You're one half of a double act.

• Translate business requirements into technical specs, and technical outputs into business narratives that clients actually understand.

• Be part of Avra's FDE cohort — benchmark across use cases, share learnings, and sharpen each other's work. You are a domain specialist, but you'll have a Data Science pair, a cohort of FDEs around you, and a direct line into how Product builds.

• Bring insight from the field back to Avra's product and engineering teams. Drive quick iteration based on real-world usage and feedback, improving our existing solutions and shaping future product offerings.

• Bridge the gap between experts and executives — communicate effectively with data specialists and operational teams as well as C-level stakeholders. Translate detail into business impact, and vice versa.

• Background in statistics, econometrics, or data science (degree or equivalent practical experience).

• 2+ years of hands-on experience solving real business problems with data — in credit, risk, fraud, growth/acquisition, or an adjacent decision-heavy domain. You've shipped work that drove decisions, not just studied it.

• Proficiency in Python and SQL; comfort working with APIs and basic software engineering concepts (version control, deployment, testing).

• Familiarity with real-world data structures and the messiness of production data.

• Experience with experimentation and causal inference: A/B tests, incrementality, quasi-experimental methods.

• Strong communication skills — you can present a model's business impact to a CFO, CRO, or Head of Risk without their eyes glazing over.

• Previous experience at a fintech or high-growth technology company.

São Paulo — hybrid, with in-person time at customer sites.

You won't be sitting behind a dashboard. You'll be in the room with leaders, understanding their problems firsthand, and building solutions that ship in weeks, not quarters. If you're the kind of person who gets frustrated when data science stays in a notebook and never reaches production — this is your role.