Lead Data Scientist — Patient Lifecycle Analytics

Menlo Ventures; New York (NY); Hybrid; USD 140,000 - 190,000; Full time

Job summary

Nourish is hiring a Lead Data Scientist to own analytics for the patient lifecycle, embedding with the Product team to uncover how patients start care, stay engaged, churn, and return. You will build production-ready models of churn and resurrection, use causal inference to isolate feature impacts, and drive business opportunities through clear, data-backed recommendations.

The role blends product analytics, experimentation, and predictive modeling in a marketplace setting.

Qualifications

• 6+ years of relevant experience owning data science or product analytics for a consumer tech product or marketplace
• Expertise in experimentation design and causal inference methods to measure feature impact
• Strong technical skills with clean, performant SQL and Python
• Curious, strategic, and outcome-oriented with ability to translate complex data into actionable insights
• Excellent communication to earn trust across cross-functional teams

Responsibilities

• Own patient lifecycle analytics end-to-end and surface insights to shape product roadmap
• Develop production-ready predictive models for churn, resurrection, and targeting
• Deliver high-impact analyses and clear recommendations with sized business opportunities
• Design and analyze experiments; apply causal inference where experiments are not feasible
• Collaborate with Product, Engineering, Design & Growth to improve analytics platforms
• Be a thought partner to Product, sizing problems and shaping team priorities

Skills

Experimentation

Causal inference

Analytics

Communication

Strategic thinking

Tools

SQL

Python

Job description

Nourish is hiring a Lead Data Scientist to own analytics for the patient lifecycle, embedding with the Product team to uncover how patients start care, stay engaged, churn, and return. You will build production-ready models of churn and resurrection, use causal inference to isolate feature impacts, and drive business opportunities through clear, data-backed recommendations.

The role blends product analytics, experimentation, and predictive modeling in a marketplace setting.