Data Scientist

Jerry.ai•New York, NY

7d•$130,000 - $150,000•Hybrid

About The Position

Jerry.ai is building the first app to manage all aspects of car ownership, starting with insurance and expanding into other verticals like home, motorcycle, and RV. The Data Science & Analytics team is a critical force multiplier, driving data insights and informing decision-making across product, growth, tech, and operations. Each business unit has an embedded Data Scientist responsible for shaping questions, analyzing data, drawing conclusions, and driving teams towards ambitious growth goals. This role involves embedding within a core business area (growth, product, AI and automation, strategic partnerships) and owning the analytical function end-to-end. The position emphasizes hands-on execution, surfacing analytical insights, driving decisions, and seeing them through to completion, rather than pure strategy.

Requirements

2+ years of experience at a consulting firm, investment bank, or high-growth technology company.
Strong analytical skills; ability to pull, structure, and interpret data independently.
Track record of owning ambiguous problems and delivering impact.
Clear, persuasive communicator who can influence decisions at the leadership level.
First principles thinker: Break ambiguous problems into clear hypotheses before touching data.
Analytically strong (comfortable with SQL or Python).
Direct communicator: Can walk a skeptical stakeholder through a complex finding and get buy-in quickly. Writes clearly, speaks precisely, and doesn't bury the lede.
Owner: Acts like their name is on the door. Feels responsible for fixing things, even if outside their swim lane.

Responsibilities

Define the right problems: Understand what business question an analysis is answering and what decision it will inform. Push back if the question is flawed.
Data and insights: Define metrics, build reports, run analyses, design experiments, and surface data insights that move your team's key metrics.
Drive outcomes: Bring recommendations to counterparts in product, growth, finance, or operations, explain findings and their implications, and propose next steps.