Data Scientist
Job description
About the role
This role partners with marketing to turn large, complex behavioral data into clear insight that directly informs strategy. The team blends clinical expertise with data science to expand access to therapy through scalable, evidence-based models that respect user privacy. You will own the analysis of large data sets from web products to extract insight and guide marketing decisions in a mission-driven environment. The position involves monitoring uplift tests for marketing, applying statistical rigor, and presenting findings that drive decisions with measurable impact. You will work within a collaborative team culture composed of data scientists, licensed clinicians, engineers, and product and marketing leaders. Success in this role requires the ability to communicate results clearly to non-technical audiences while maintaining methodological rigor. You will build and support models that predict outcomes and influence product and marketing strategies on a mental health platform. The role sits at the intersection of experimentation, causal inference, and business impact in a remote-first, sustainable work environment.
Key facts
What you'll do
Large data sets from web products are analyzed to extract insight and guide marketing decisions.
Uplift tests for marketing are monitored and analyzed with statistical rigor, and findings are presented to drive decisions.
Results are communicated clearly to non-technical audiences as part of a collaborative team culture.
Requirements
A BSc/MA in a quantitative discipline such as Statistics, Math, Economics, Computer Science, Operations Research, or Engineering is required.
At least 3 years of experience in quantitative data analysis focused on user behavior, marketing, or product is required.
Strong expertise in statistics, especially hypothesis testing and experiment design, is required.
Experience with A/B tests and advanced methods such as difference-in-difference, regression discontinuity, or panel methods is required.
Experience building predictive and causal inference models is required.
An advanced proficiency in R and Python for data analysis is required.
The ability to support or influence business decisions through data analysis is required.
Clear communication skills are required to simplify complex topics for non-technical audiences.
Advanced experience with SQL is required.
Nice to have
Experience with Bayesian modeling methods is valued.
Experience with Marketing Mix Models and Multi-Touch Attribution models is valued.
Experience analyzing two-sided marketplaces is valued.
Experience with B2C marketplace products is valued.
Familiarity with BI tools like Tableau and Looker is valued.
Practical notes
This role is fully remote with occasional in-person collaboration.
Typical interview steps
Data interviews commonly include a SQL or coding exercise, a statistics question, and a case study. Candidates may be asked to design a metric, interpret an experiment, or build a small model. Some companies give a take-home analysis. Expect questions about past projects and the business impact of your work. Interviewers often evaluate how you communicate uncertainty and business impact, not only the math. Bringing a clean write-up of a past analysis to the interview is well received.
Good to know
Data scientists in this role work with modern analytics tools such as R and Python to turn raw web data into decisions.
The role sits at the intersection of product, marketing, and experimentation to improve user engagement in a mental health platform.
Statistical experimentation methods and causal inference are central to the day-to-day work.
Collaboration across clinicians, engineers, and marketers shapes how insights translate into product and marketing actions.
The role emphasizes sustainable work habits within a remote-first, mission-driven environment.
Career growth
Data careers grow toward senior analyst, staff data scientist, or data engineering lead. Many professionals specialize in machine learning, analytics, or infrastructure. Cross-functional work with product and engineering teams becomes more important at senior levels. The field changes quickly, so continuous learning is part of the job. Professionals who can translate numbers into decisions tend to advance fastest.