Data Scientist - Business & Product

The Role

You'll be part of a lean, high-ownership data team at Medal, working directly with business and product leadership. You'll own how we learn about our users end-to-end: the company-wide testing roadmap, our analytics instrumentation, the data pipeline, and KPI reporting, plus the deep dives and thought-leadership publications that come out of it. You'll set your own roadmap, evangelize the data so everyone understands it better, and have real influence on what we build next.

You design and analyze experiments end to end: the hypothesis, the sample size, guardrail metrics, control configuration, and the readout. You configure test structure to yield the right information and manage a complex multi-test pipeline where several things run at once, and you run causal analysis when a clean A/B test isn't possible.

You build the strategy behind our analytics instrumentation and own the collection and reporting of the company's key performance indicators, working with our front-end engineers to build telemetry and data scaffolding when tracking is wrong or missing. You inform the quant behind pricing, including willingness to pay, conjoint, price elasticity, and offer testing in upsells and bundles. And you are the data backbone for industry and brand thought leadership, both co-published with partners and self-published.

Across the board you touch analytics and statistical analysis cross-functionally, informing business decisions such as advertising incrementality, subscription pricing, and conversion as well as product decisions. Your recommendations come with a confidence interval and an effect size.

What We're Looking for

• 3 to 5 years of experience managing and researching product analytics, or a master's degree in statistics or a related field.
• Applied statistics depth: regression, experimental design, and causal inference. Bayesian methods are a plus.
• Strong SQL, Python, or R, with experience on event-level data at consumer scale and data warehouse tooling such as BigQuery, Snowflake, or Airflow.
• Fluent in product analytics platforms like Amplitude and business intelligence tools like Tableau, or the equivalents.
• Comfortable coordinating with engineers on release cycles in a CI/CD environment.
• You use AI tools to raise the bar on your analysis, and you are the kind of person who checks whether the AI got it right.
• You ask why until why is exhausted, and you know what the data cannot answer.
• Great communication and storytelling, and the ability to manage your own roadmap.
• Bonus: an ability to conduct qualitative UX research.