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Responsible for scaling the consumer finance platform, improving API performance, integrating with third-party APIs, and enhancing user experience for millions of users.

Design, build, validate, deploy, and monitor ML models (credit risk, fraud, transaction timing). Create training datasets, run A/B tests, analyze customer behavior, and collaborate with Engineering, Product, and Analytics to deliver end-to-end data science solutions.

Build, test, deploy, and monitor ML models for credit risk, fraud detection, and transaction timing. Create training datasets, run experiments and A/B tests, analyze customer trends, and collaborate with Engineering, Product, and Business teams to deliver end-to-end data science solutions.

Lead development, deployment, and monitoring of underwriting, risk, fraud, and payments ML models. Own full modeling lifecycle, feature engineering, validation, A/B testing, cross-functional collaboration, and mentor junior data scientists.

Lead and own end-to-end ML modeling for underwriting, fraud, and payments: build, validate, deploy, monitor models; create training datasets; run A/B tests; analyze customer trends; establish feature and model best practices; and mentor junior data scientists while collaborating with Engineering, Product, and Business teams.

Support partner onboarding and operations for affiliate marketing: set up links/offers, coordinate CRM campaigns, maintain dashboards and workflows, reconcile partner revenue, and provide ad-hoc partner reporting and launch support.

Lead and scale Brigit's IT Operations: manage day-to-day support, own IAM/SSO (Okta), maintain VPN and security tooling (SIEM, IDS/IPS, firewalls), drive audit readiness (SOC, PCI, SOX), improve incident response, document processes, and partner cross-functionally on security and compliance.

Lead and grow a data science team building predictive models for credit risk and payments. Drive integration with product and engineering, run quarterly planning, mentor scientists, adopt AI-assisted and agentic workflows, and collaborate on model infrastructure and deployment.

Lead and grow a data science team to build predictive models for credit risk and payments; mentor scientists; integrate DS work with product, engineering, and operations; run quarterly planning with stakeholders; drive adoption of AI-assisted and agentic workflows with validation and quality standards; and collaborate with modeling engineering to improve model research, development, and deployment infrastructure.

The Senior Analytics Engineer will manage the analytics layer, owning dbt projects, orchestrating data pipelines, defining metrics, and ensuring data quality while mentoring the data team.