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# Data Scientist

- **Company:** Flagler Hospital, Inc.
- **Location:** New York, NY, United States (Remote available)
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** EHealth, Python (Programming Language), Standard Sql
- **Published:** September 4, 2026
- **Apply:** https://startup.jobs/senior-data-scientist-flagler-health-9276805

## About the Role

* 3+ years of experience in data science or analytics, ideally in a product-driven environment.
 * BS or equivalent, with a track record of strong analytical and logical reasoning.
 * Demonstrated depth in product or operational analytics: retention analysis, cohort analysis, funnel and engagement measurement.
 * Strong SQL and fluency in Python or R for analysis.
 * Genuine problem-solving orientation. You are comfortable with ambiguous questions and can define the analysis yourself.
 * Excellent communication and storytelling. You can walk through a problem you solved, the approach you chose, and why, and make it land with a non-technical audience.
 * Comfort operating in a fast-moving startup where the question changes before the dashboard is finished.

Nice to Have

 * Experience in healthcare, digital health, or another regulated data environment.
 * Experience building analytics infrastructure from an early stage.

Work Environment

 * NYC HQ preferred. Remote is possible for exceptional candidates.
 * We have video on for our weekly team syncs and technical discussions. Camera is required to be on during meetings.

## Description

* Own product and operational analytics across the platform, including retention, cohort behavior, funnel performance, and engagement.
 * Define and maintain the core metrics the company runs on, and make sure they mean the same thing to everyone.
 * Partner with Product and Engineering to instrument new features correctly and measure whether they worked.
 * Build analyses that answer open-ended operational questions, not just recurring reports.
 * Translate findings into clear recommendations for technical and non-technical audiences, including leadership.
 * Investigate anomalies in the data and drive them to root cause.
 * Build and maintain the dashboards, models, and data hygiene practices that make analysis repeatable.


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