- Lead the design and selection of statistical and ML models for risk scoring, fraud detection, and transaction categorization.
- Use agentic coding and research tooling to explore data, test feature hypotheses, scaffold pipelines, and build model services.
- Work with Product and business teams to identify useful signals in financial data and define modeling problems.
- Build or shape production pipelines, APIs, and services for models.
- Monitor live models, manage drift and retraining, and retire models when evidence supports doing so.
- Design and run A/B tests and offline evaluations to assess model changes.
- Partner with Engineering, Data Research, Product, Intelligence Squad members, business stakeholders, and customers.
- Explain modeling trade-offs and impact to non-technical audiences, including executives.
- Keep current with GenAI releases, research, and tooling, and share useful findings through demos, documentation, or company sessions.
- Determine when rules, heuristics, or improved data pipelines are preferable to modeling.
PythonSQLMachine Learning+5 more