Product Data Scientist

Clair•New York, NY

20h•Hybrid

About The Position

As a Product Data Scientist at Clair, you’ll own the experimentation and analytics layer that drives our product and underwriting decisions. You’ll play a critical role in shaping how we balance growth, risk, and user experience by designing experiments, defining success metrics, and translating data into actionable product strategy. This role sits at the intersection of product, finance, and risk. You’ll act as the central owner of all A/B testing at Clair — from underwriting experiments (e.g., advance limits, accrual velocity) to product surface testing that impacts user behavior and downstream credit outcomes. Beyond experimentation, you’ll define how we measure success, build forecasting frameworks, and ensure that product decisions are grounded in strong unit economics. We’re looking for someone with strong business intuition, deep expertise in experimentation and statistics, and the ability to translate complex data into clear strategic recommendations. This is less about building machine learning models and more about driving decision-making through rigorous analysis, experimentation, and cross-functional influence.

Requirements

5+ years of experience in data science, product analytics, or a related analytical role.
Strong foundation in statistics and experimental design, including A/B testing, causal inference, and hypothesis testing.
Proven experience owning end-to-end experimentation programs and influencing product decisions through data.
Strong SQL skills and experience working with large datasets.
Demonstrated ability to translate complex analyses into clear business insights and recommendations.
Experience working cross-functionally with Product, Finance, or Strategy teams in a fast-paced environment.
Strong business intuition and ability to think in terms of trade-offs, unit economics, and growth vs. risk.

Nice To Haves

Experience in fintech, lending, or credit-related products.
Familiarity with underwriting concepts such as risk scoring, approval strategies, and loss modeling.
Experience building forecasting models for business or financial metrics.
Proficiency in Python or R for data analysis.
Experience with experimentation platforms and statistical tooling.

Responsibilities

Own and manage Clair’s experimentation ecosystem, including the design, execution, and analysis of A/B tests across underwriting and product experiences.
Translate model outputs (e.g., risk scores) into actionable business decisions, such as approval thresholds and lending strategies.
Serve as the single source of truth for all experiments, ensuring consistency, rigor, and proper interpretation of results across the organization.
Design experiments that evaluate key levers such as credit limits, accrual mechanisms, and pricing, and quantify their impact on both growth and risk metrics.
Define and standardize core product and financial metrics, including how we measure model impact, user behavior, and unit economics.
Partner closely with Product, Finance, Risk, and Engineering teams to align on strategy, evaluate trade-offs, and inform decision-making.
Act as a strategic advisor, helping stakeholders understand the implications of experiments and guiding data-driven product development.