Data Scientist: Product

Thatch Data Scientist: Product

Location

Austin, Texas; New York, New York; San Francisco, California; Remote (US)

Compensation

$160,000 - $230,000 USD

About Thatch

For most people, health insurance is something that happens to them: a plan chosen by someone else, with little room to fit their life. Thatch is changing that. We help companies give every employee a real healthcare budget instead of a one-size-fits-all plan, so people can choose the coverage and care that works for them, and employers get predictable costs they can control.

It’s a simple idea, but making it real means solving hard problems across healthcare, payments, and technology, and we’re just getting started. We look for people who like solving complex problems, use good judgment, and are comfortable working through ambiguity. People here care deeply about our customers, take real responsibility for what gets built, and are driven by doing work that matters.

About the role

Thatch is changing how health benefits work. We're building software that makes it easier for businesses to offer health coverage and gives employees more choice and control.

We're looking for a Data Scientist to help our Product and Operations teams understand how people use Thatch and where we can make the biggest impact. You'll own the data models and metrics behind our product, from plan shopping and enrollment to reimbursements and everyday benefit usage.

You'll dig into user behavior, uncover friction, and design experiments that help us decide what to build and improve next. This is a hands-on role where you'll build the analytics foundation teams rely on, turn complex data into clear recommendations, and help shape our product strategy.

What you will do

• Partner with Product and Operations to answer important questions and inform product priorities.
• Define and own product metrics, from company-wide success measures to feature-level performance.
• Analyze user journeys, engagement, and operational data to uncover friction and opportunities.
• Design and evaluate experiments to measure the impact of product changes.
• Build and maintain dbt models, metrics, dashboards, and analytical tools that teams rely on.
• Improve data quality, instrumentation, and self-serve analytics so teams can make confident decisions.

What we are looking for

• Experience using analytics, data science, and experimentation to influence product decisions.
• Strong SQL skills and experience with dbt or similar data transformation tools.
• Experience building analytical data models and dashboards using BI or visualization tools.
• Proficiency in Python for analysis, experimentation, or statistical modeling.
• Strong product judgment and the ability to turn complex findings into clear, actionable recommendations.
• Comfortable working independently, navigating ambiguity, and partnering across teams.

Tools and tech stack

• Data: Snowflake, SQL, dbt
• Analytics: Omni, Hex, Python
• Development: GitHub, CI/CD
• AI: Claude, ChatGPT, Copilot, and other tools that help us work faster and smarter

You won't work directly in our application layer day-to-day, but familiarity with source data and event instrumentation from systems like Ruby on Rails is helpful.

Experience that stands out

• Influencing product roadmaps and prioritization through data.
• Building experimentation practices, including guardrail metrics, sample-size guidance, or analysis frameworks.
• Analyzing funnels, retention, cohorts, or engagement across complex user journeys.
• Building statistical or machine-learning models for segmentation, forecasting, propensity, or recommendations.
• Experience in healthcare, fintech, or other regulated industries.

What to expect

We aim to move quickly, and most candidates complete the process within 2–3 weeks. You can expect:

• An initial conversation with a recruiter.
• A conversation with the hiring manager and a lightweight Python coding exercise.
• A take-home analytics exercise.
• A review of your work, a data modeling and metrics exercise with the team, and a discussion with cross-functional partners.
• A final conversation with engineering and company leadership.

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