Data Scientist, Business & Product

Role Overview

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.

Key Responsibilities

• Design and analyze experiments end‑to‑end: help affirm the team’s hypothesis, design experiment sample size, guardrail metrics, control configuration, and the resulting readout; configure test structure to yield the right information and manage a complex multi‑test pipeline; run causal analysis when a clean A/B test isn’t possible; understand that multiple tests may run simultaneously.
• 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, work with front‑end engineers to build appropriate telemetry and data scaffolding.
• Hunt for opportunities in data that can inform strategy.
• Communicate and evangelize data, helping everyone have a better understanding; recommendations include a confidence interval and effect size.
• Participate in team planning and road‑mapping by contributing insights and expertise.
• Inform the quant behind pricing, including willingness to pay, conjoint analysis, price elasticity, and offer testing in upsells and bundles.
• Serve as 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.
• Apply analytics and statistical analysis in a cross‑functional capacity to help inform both business decisions (advertising incrementality, subscription pricing, conversion) and product decisions.

Qualifications

• 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.
• Comfortable coordinating with engineers on release cycles in a CI/CD environment.
• Use AI tools to enhance productivity and raise the bar on analysis.
• Experience with event‑level data at consumer scale and data‑warehouse tooling such as BigQuery, Snowflake, or Airflow.
• Fluent in product analytics platforms like Amplitude or business‑intelligence tools like Tableau (or similar).
• Strong SQL, Python, or R for analysis code.
• Great communication and storytelling skills.
• Strong ability to manage your own roadmap.
• Bonus: Ability to conduct qualitative UX research.

Culture

• Need for speed; comfortable with a fast‑paced, action‑oriented environment.
• Ability to communicate risks and trade‑offs between measurement rigor and shipping velocity.
• Awareness of the limits of data and willingness to acknowledge what data cannot answer.
• Habit of verifying AI‑generated analysis for correctness.
• Persistent curiosity: ask “why” until fully satisfied.
• Hunger to learn and test yourself; help level up the organization.
• Proactive mindset: when you see something on the ground, you pick it up.