Data Scientist, Analytics

Location: Austin, Texas, United States; New York, New York, United States; Remote (US); San Francisco, California, United States

Department: Engineering

About the role

Thatch is rewiring how health benefits work. We build software that gives employees real control over how they use their benefits.

Were looking for a Data Scientist to help Thatch make better product, commercial, financial, and operational decisions. Youll own both halves of the problem: the data models and measurement systems that make rigorous analysis possible, and the analysis itself - investigating business questions and recommending what we should do next. This work touches real money and real healthcare decisions, so the numbers need to hold up. But so does making them simple to use for the teams who depend on them.

This is a hands-on, full-stack analytics role. Depending on your experience, you may focus more on building core analytics and internal tools, or take on broader ownership of measurement, experimentation, and strategic decision support.

What you will do

• Partner with stakeholders across Product, GTM, Finance, and Operations to turn ambiguous questions into clear analytical approaches and recommendations.
• Analyze product, customer, funnel, marketing, and financial data to explain performance and identify opportunities.
• Own how we measure go-to-market performance by building trusted metrics that connect attribution, CRM, and product usage data.
• Build and maintain the dbt models, metrics, dashboards, and analytical tools the company runs on.
• Improve data quality and enable self-serve analytics through testing, monitoring, documentation, and better data products. 

What we are looking for

• Experience using data to answer business questions and influence decisions.
• Strong SQL skills and experience with dbt or similar data-transformation tools.
• Experience building analytical data models and communicating insights through BI or visualization tools
• Working knowledge of Python for analysis, automation, or statistical modeling.
• Strong stakeholder partnership, communication, and judgment when balancing speed, rigor, and maintainability

We hire across levels and care most about the scope you’ve owned, the impact you’ve had, and how you make decisions.

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

• Partnering closely with Growth, Marketing, Revenue Operations, or other GTM teams on acquisition, funnel, lifecycle, or marketing analytics.
• Designing and analyzing geo experiments or other quasi-experimental studies when randomized experiments arent feasible.
• Building or applying statistical models such as lead scoring, forecasting, or segmentation.
• Creating internal tools or automations that improve how teams work.
• Working in healthcare, fintech, or other complex, regulated domain. 

How we work

• We move quickly and are biased toward action.
• We ship early, learn from real usage, and iterate.
• We operate with trust, ownership, and high empathy.
• We value clarity over ceremony and quality over shortcuts.

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.