Location
Austin, Texas; New York, New York; San Francisco, California; Remote (US)
Compensation
$190,000 - $250,000 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 GTM and Marketing teams understand what's driving growth and where to invest next. You'll build the data models and measurement systems behind our growth funnel, from acquisition and conversion to enrollment, retention, and expansion.
You'll dig into performance, uncover opportunities, and design experiments to help us make smarter growth decisions. This is a hands-on role where you'll work closely with teams across the business, turn complex data into clear recommendations, and help shape Thatch's growth strategy.
What you will do
• Partner with GTM and Marketing to answer important business questions and guide growth decisions.
• Analyze acquisition, conversion, retention, and marketing performance to identify opportunities.
• Own GTM measurement, connecting attribution, CRM, and product data into trusted metrics.
• Build and maintain dbt models, dashboards, and analytical tools that teams rely on.
• Design and evaluate experiments to understand what's working and where to invest.
• Improve data quality and make insights more accessible through self-serve analytics and automation.
What we are looking for
• Experience using analytics, data science, and experimentation to drive growth, marketing, or GTM outcomes.
• 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 business 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
• dbt and SQL for modeling.
• Snowflake as our data warehouse (migrating from Redshift).
• Omni for BI, with tools like Hex for ad hoc analysis.
• GitHub with CI/CD for version control.
• AI tools (e.g., Claude, ChatGPT, Copilot) are woven into our daily development and analysis workflow.
You won’t work directly in our application layer day-to-day, but understanding source data from systems like Ruby on Rails is important.
Experience that stands out
• Experience with Growth, Marketing, or Revenue Operations analytics, including acquisition, attribution, and lifecycle performance.
• Measuring marketing incrementality through holdouts, geo experiments, lift tests, or similar methods.
• Building statistical or machine-learning models for forecasting, segmentation, lead scoring, or churn.
• Creating tools and automations that help teams make better, faster decisions.
• 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.