Data Science Lead: Patient Lifecycle & Growth Analytics

Usenourish; New York (NY); On-site; USD 180,000 - 240,000; Full time

Job summary

Nourish in New York City is seeking a Data Science Lead to embed with our Product team, owning analytics across the patient lifecycle to unlock growth and improve outcomes.

You will build production-ready models of churn and resurrection, apply causal inference, and contribute to our experimentation platform while collaborating with Product, Engineering, Design, and Growth to translate insights into measurable opportunities.

Qualifications

• 6+ years of relevant experience in data science or product analytics.
• Expertise in experimentation and causal inference.
• Strong SQL and Python skills.
• Ability to translate data into actionable insights and influence product decisions.
• Experience in consumer tech or marketplace environment is a plus.

Responsibilities

• Own patient lifecycle analytics end-to-end: Define metrics and surface insights that shape our understanding of patient behavior 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 apply causal inference methods to measure impact when experiments aren’t feasible.
• Be a thought partner to Product: Work backwards from sizing a problem to proposing actions and shaping roadmaps.
• Contribute to our experimentation platform: Help build the methodology, standards, and tooling used across teams.

Skills

SQL

Python

Data science

Experimentation

Causal inference

Product analytics

Communication

Job description

Nourish in New York City is seeking a Data Science Lead to embed with our Product team, owning analytics across the patient lifecycle to unlock growth and improve outcomes.

You will build production-ready models of churn and resurrection, apply causal inference, and contribute to our experimentation platform while collaborating with Product, Engineering, Design, and Growth to translate insights into measurable opportunities.