Staff Data Analyst
Grow TherapySan Francisco$182k – $227kPosted 16 April 2026
Job Description
About Us:
Grow Therapy is on a mission to serve as the trusted partner for therapists growing their practice, and patients accessing high-quality care. Powered by technology, we are a three-sided marketplace that empowers providers, augments insurance payors, and serves patients. Following the mass increase in depression and anxiety, the need for accessibility is more important than ever. To make our vision for mental healthcare a reality, we’re building a team of entrepreneurs and mission-driven go-getters. Since launching in February 2021, we’ve empowered more than ten thousand therapists and hundreds of thousands of clients across the country and insurance landscape. We’ve raised more than $178mm of funding from Sequoia Capital, Transformation Capital, TCV, SignalFire, and others.
The Opportunity:
Grow Therapy is seeking a Staff Data Analyst to drive data-informed decision-making across our Match Success product group. In this role, you will lead analytical initiatives that help us understand how patients and therapists connect and begin their journey together, and shape the product experiences that make therapy accessible and effective. You will partner closely with Product and cross-functional stakeholders to set the analytical agenda, own business-critical reporting, use data to improve products, and run rigorous experiments, while helping raise the bar for how data is used across the team.
What You’ll Be Doing:
Partner with Product and other technical stakeholders to prioritize analytical needs and support product launches, decision-making, and ad hoc requests through clear, data-driven insights.
Own analytical strategy and define the metrics, reporting frameworks, and experimentation roadmap that guide product direction and OKR development for the team
Design, maintain, and scale business-critical dashboards and datasets that give stakeholders reliable, self-serve access to the metrics that matter.
Lead experimentation end-to-end, from thoughtful test design through execution, analysis, and clear, actionable recommendations that drive product decisions.
Craft compelling data narratives and visualizations that translate complex findings into crisp takeaways for product, leadership, and executive audiences.
Collaborate with data scientists and data engineers to improve logging, pipeline reliability, data availability, and modeling foundations (including dbt).
Partner with other analysts, contribute to shared tooling and frameworks, and help set standards for analytical rigor across the team.
You’ll Be a Good Fit If:
You have 7+ years of experience applying analytics to complex, cross-functional business problems in a high-growth, product-focused environment.
You are a self-directed owner who proactively identifies high-impact opportunities, navigates ambiguity well, and drives work forward without waiting to be asked.
You are deeply fluent in SQL and have strong working knowledge of experimental design, including A/B testing methodology and statistical interpretation.
You have a proven track record building business-critical dashboards and data visualizations, ideally in Hex, Looker, or similar platforms.
You have experience with data modeling (dbt) or a strong track record of quickly picking up new technical tools and frameworks.
You are a skilled data storyteller who knows how to extract insight from complexity and communicate it clearly to both technical and executive audiences.
You build strong relationships across functions, comfortable partnering with engineers, PMs, and executives, and you know how to earn trust through rigor and clear communication.
You care deeply about expanding access to mental health care and are motivated by work that has a direct impact on patient and provider outcomes.`
Experience with advanced analytics techniques such as python, ML models, LLM, etc., is a plus.
Role Details:
Employment Type: Full-Time, Exempt
Base Compensation: The base compensation range for th ... (truncated, view full listing at source)
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