Data Scientist (Business & Product)

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. Design and analyze experiments end-to-end: help affirm the team’s hypothesis and design experiment sample size, guardrail metrics, control configuration, and the resulting readout. You will configure test structure to yield the right information and manage a complex multi-test pipeline. You know how to run causal analysis when a clean A/B test isn’t possible. You understand that multiple things will run at the same time.

You help build the strategy behind our analytics instrumentation and facilitate the collection and reporting of the company’s key performance indicators. When tracking is wrong or missing, you work with our front‑end engineers to build appropriate telemetry and data scaffolding. You will hunt for opportunities in data that can inform strategy. You will communicate and evangelize against the data and help everyone have a better understanding. Your recommendations include a confidence interval and effect size.

You will participate in team planning and roadmapping by contributing your insights and expertise. You inform the quant behind pricing, including willingness to pay, conjoint, price elasticity, and offer testing in upsells and bundles. You are the data backbone for industry and brand thought leadership (Medal trends and how they line up with macro trends), both co‑published with partners and self‑published.

Across the board, you will touch analytics and statistical analysis in a cross‑functional capacity to help inform both business decisions (advertising incrementality, subscription pricing, and conversion) and product decisions. Great communication and storytelling skills are essential. You are comfortable coordinating with engineers on release cycles in a CI/CD environment. Strong ability to manage your own roadmap.

Experience with event-level data at a consumer scale and data warehouse tooling such as BigQuery, Snowflake, or Airflow. Strong SQL, Python, or R for analysis code. 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 (e.g., feature A/B tests), and causal inference. Bayesian methods are a plus.

You use AI tools to enhance your productivity and raise the bar on your analysis. Bonus: An ability to conduct qualitative UX research. Fluent in product analytics platforms like Amplitude or business intelligence tools like Tableau or tools similar to these.

You are the kind of person who asks why until why is exhausted. You have a need for speed and are comfortable with a fast‑paced culture and a team that leans towards action. You are the kind of person who checks whether your AI analysis is correct. You also know what the data cannot answer. You are hungry to learn and test yourself. You help level an organization up. When you see something on the ground, you pick it up. As a custodian of analysis, you can communicate risks and tradeoffs in measurement vs. shipping velocity.