Staff Data Scientist
Nourish
Salary undisclosed
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Description
About Us
Our mission is to improve people’s health by making it easy to live a healthy lifestyle.
Nourish is the country's largest dietitian-led metabolic health clinic. We’re an AI-native digital health system matching patients with 10,000+ Registered Dietitians, physicians, medications, lab testing, and AI agents to deliver insurance-covered care across all 50 states. Founded four years ago, we've completed millions of appointments, tripled year-over-year, and partnered with health plans covering 200M+ Americans across 250+ health systems.
In 2026 we raised a $100M Series C, bringing total funding to $215M. The round was led by Menlo Ventures, with participation from Thrive Capital, Index Ventures, J.P. Morgan Growth Equity Partners, Maverick Ventures, Y Combinator, BoxGroup, Atomico, Daybreak, and Operator Partners.
Learn more about our Series C here: Nourish Blog, Bloomberg, Fierce Healthcare, Digital Native, The Pulse Podcast.
This is not a job for everyone. We hold an extremely high bar because we believe talent density is our biggest competitive advantage. We're looking for people who actively choose hard, ambiguous problems, who run toward unglamorous work, give and receive candid feedback, and bring relentless resilience without the ego. Our work is important, but we are not self-important. We do this because we’re solving one of the hardest problems in the world, and the problem matters. If that's you, we disproportionately reward it.
About the Role
We're scaling quickly, and data is one of our most important strategic levers for unlocking growth and improving patient outcomes. As a Staff Data Scientist, you'll embed with our Product team and own analytics for a specific, business-critical function—developing a deep understanding of our patients and their needs and turning that understanding into business opportunities.
This role blends product analytics, experimentation, and predictive modeling in a marketplace setting: you'll build and validate production-ready models, use causal inference to isolate the impact of features and feature adoption, and produce deep dives that end in clear recommendations with sized business opportunities. You'll also contribute to the development of our experimentation platform, and collaborate closely across our Product, Engineering, Design & Growth teams.
Our data team is based in NYC and goes into the office 2-3 days a week. While we don't require a specific number of days in office, we're looking for people who would be excited to join us onsite.
Key Responsibilities
- Own data and analytics end-to-end: Define the metrics and surface the insights that shape our understanding of our business and inform the product roadmap
- Develop production-ready predictive models: Build, validate, and maintain models of churn, resurrection, and targeting
- Deliver high-impact analyses: Break down complex topics into clear narratives and recommendations with sized business opportunities
- Drive scientific decision-making: Design and analyze experiments, and where experiments aren't feasible, apply causal inference methods to measure the impact of features and feature adoption
- Be a thought partner to Product: Work backwards from sizing a problem to proposing what we should do about it, and shape team priorities and roadmaps
- Contribute to our experimentation platform: Help build the methodology, standards, and tooling that teams across the company rely on
We'd Love to Hear From You If You...
- Have 6+ years of relevant experience: You've owned data science or product analytics for a product area, ideally at a consumer tech company or a marketplace
- Are an experimentation and causal inference expert: You've designed and analyzed experiments end-to-end, and applied causal inference methods where experiments weren't feasible
- Bring strong technical skills: You write clean, performant SQL, and are proficient in Python
- Are curious, strategic, and outcome-oriented: You proactively shape the right questions, translate complex data into clear, actionable insights, and balance speed with depth to drive high-impact decisions
- Communicate complex ideas with clarity: You distill deep analysis into crisp, actionable takeaways, and you earn trust across cross-functional teams
- Are motivated by impact and mission: You believe in the power of food as medicine to transform healthcare and are excited to improve lives at scale by contributing to Nourish's mission
More Information
Benefits
- >Benefits
About this role
Nourish is building a digital health system that connects patients with dietitians, physicians, and AI to deliver metabolic health care across the country. As a Staff Data Scientist, you'd be embedded with the Product team owning analytics for a core business function—understanding patient needs and translating that into growth opportunities. This role sits at the intersection of product analytics, experimentation, and predictive modeling in a marketplace setting, where you'd build production models for churn and targeting, design and analyze experiments, apply causal inference to measure feature impact, and deliver analyses that end in clear, sized business recommendations.
The work demands someone with 6+ years of experience owning data science or product analytics at a consumer tech or marketplace company, with genuine expertise in experimentation and causal inference. You'll need strong technical fundamentals—clean SQL, Python proficiency—but the role is equally about strategic thinking: asking the right questions, translating complexity into crisp narratives, and earning trust across Product, Engineering, Design, and Growth teams. You'd also help build the experimentation platform and methodology that other teams rely on.
This is an in-office role in New York City with a team that goes in 2-3 days a week. Nourish is well-funded, scaling quickly, and explicitly looking for people drawn to hard problems and unglamorous work who can operate with high autonomy and candor. If you're motivated by the mission of food as medicine and want your data work to directly shape how millions of Americans access healthcare, this is worth a close look.
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