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Salary: USD 184,000 - 225,000 / annual
Flex is a growth-stage fintech company headquartered in NYC that is transforming how renters pay rent by enabling flexible, on-time payments throughout the month. The company is now expanding into a broader consumer bill management platform—a hub where users manage recurring and one-off bills, align payments with their cash flow, and build credit.
As Senior Staff Data Scientist for New Verticals, you will operate as both the architect of the data foundation and the analyst driving data-informed decisions. You'll work closely with business leadership and the core data team to establish modeling standards, analyze consumer behavior, and scale the data infrastructure as the product moves from pilot to full launch.
Key responsibilities include:
- Build and maintain core data models using dbt and Snowflake that power reporting, experimentation, and ML/credit risk use cases
- Analyze consumer bill payment behavior, splitting patterns, delinquency, and credit-building outcomes to identify what's working in the pilot and what needs refinement
- Partner with Product and Engineering to instrument new features (bill splitting, smart scheduling, credit-building) so adoption and impact can be measured from day one
- Design and analyze experiments testing pricing, credit terms, and product flows to establish causal links between changes and business outcomes
- Create dashboards and self-serve analytics tools enabling Product, Risk, and GTM teams to track pilot-to-scale metrics independently
- Champion data quality, consistent terminology, and documentation as the data footprint grows
- Set technical direction for how data models and pipelines scale with new billers, partners, and product surfaces; mentor other analysts and engineers
You bring 7+ years spanning analytics engineering and data science/analyst work, with strong SQL, hands-on dbt and Snowflake experience, and proficiency with BI tools (Sigma, Tableau, Mode, Looker). You have a proven track record designing and analyzing experiments (A/B tests, quasi-experimental methods) to drive product decisions, and you're confident partnering with both technical and non-technical stakeholders. Experience with credit, risk, or fintech data is a plus.
