Sr Staff Data Scientist, New Verticals

Flex•New York, NY

5d•$184,000 - $225,000•Hybrid

About The Position

Flex is a growth-stage, NYC headquartered FinTech company that is creating the best rent payment experience. Flex enables our users to pay rent throughout the month on a schedule that better fits their finances and budget. Our mission is to empower as many renters as possible with flexibility over their most significant recurring expense. Flex is expanding to offer a consumer bill management platform: a hub where users manage both recurring and one-off bills, align payments with their cash flow, and build credit along the way. As we move from a pilot to broader GTM, we're looking for someone who can operate as both the builder of our data foundation and the analyst who turns that data into decisions. This role will work closely with both business leadership and the core data team to focus on modeling data, digging into consumer behavior, pricing and credit dynamics, and setting the standards the rest of the team builds on.

Requirements

7+ years of experience spanning analytics engineering and data science/analyst work
Strong SQL and hands-on experience with dbt and Snowflake (or equivalent modern data stack)
Experience with BI tools (Sigma, Tableau, Mode, Looker, etc.) and building dashboards that non-technical stakeholders use
Track record designing and analyzing experiments (A/B tests or quasi-experimental methods) to drive product decisions
Being confident partnering with technical and non technical stakeholders
Strong ownership mindset

Nice To Haves

Comfort with credit, risk, or fintech data, as well as curiosity about how consumers manage their finances, is a plus

Responsibilities

Build and maintain the core data models (dbt/Snowflake) that power reporting, experimentation, and ML/credit risk use cases
Analyze consumer bill payment behavior timing, splitting, delinquency, credit-building outcomes to identify what's working in the pilot and what needs to change
Partner closely with Product and Engineering to instrument new features (e.g. bill splitting, smart scheduling, credit-building) so we can measure adoption and impact from day one
Design and analyze experiments that test pricing, credit terms, and product flows, establishing causal links between changes and outcomes
Create dashboards and self-serve tools that let Product, Risk, and GTM teams track Control Center's pilot-to-scale metrics without needing to come back to Analytics for every question
Champion data quality, consistent terminology, and documentation as data footprint grows from pilot to full launch
Set technical direction for how Control Center's data models and pipelines scale as new billers, partners, and product surfaces come online; mentor other analysts/engineers and raise the technical bar across the team