Staff Data Scientist

Jerry.ai•New York, NY

5d•$170,000 - $210,000•Remote

About The Position

Jerry.ai is seeking Staff Data Scientists to join their Data Science & Analytics team. This team acts as a force multiplier, driving data insights and informing decision-making across product, growth, tech, and operations. Data Scientists are embedded within business units to define problems, conduct analyses, design experiments, and drive outcomes. The role involves both analytical insights and hands-on execution, with the goal of helping Jerry.ai scale from 5 million to 50 million customers and become a $10 billion business in the next four years. The company is expanding into new verticals like home, motorcycle, and RV, building an AI agent to manage all physical assets.

Requirements

6+ 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.
Comfortable with SQL or Python.
First principles thinker: breaks ambiguous problems into clear hypotheses before touching data.
Direct communicator: can walk a skeptical stakeholder through a complex finding and get buy-in quickly.
Owner: acts like their name is on the door and feels responsible for fixing things, even if outside their swim lane.

Responsibilities

Sit on the central Data Science & Analytics team but be embedded in one of Jerry's core business areas (growth, product, AI and automation, strategic partnerships) and own the analytical function for that business unit end-to-end.
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.