belvo - Senior Applied Data Scientist

• 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.