Data Scientist — Minneapolis, MN

About The Role

The role centers on turning messy, high-volume data into models and analyses that drive product and business decisions - experimentation design, causal inference, forecasting, and statistical modeling in a production setting.

The team is small, senior, and directly embedded with product and engineering; analyses and models from this role ship to production, not just slide decks.

Key Responsibilities

• Design and analyze A/B tests and quasi-experiments, including power analysis, sequential testing, and difference-in-differences when randomization isn't feasible

• Build production-grade models (classification, forecasting, uplift) using Python, SQL, and scikit-learn, partnering with ML engineers to deploy and monitor them

• Develop deep understanding of product and behavioral data; own the metric definitions, dashboards (Tableau/Looker), and anomaly detection that keep the organization aligned

• Apply causal inference methods - propensity scoring, instrumental variables, synthetic control - to high-stakes business questions with no clean experimental data

• Write performant SQL and PySpark pipelines on large-scale datasets (Snowflake, Databricks), ensuring reproducibility and data quality

• Communicate findings clearly to engineering, product, and executive audiences - with quantified uncertainty, not overconfident point estimates

• Contribute to the team's statistical methodology, tooling, and code review standards

What We Are Looking For

• 3–7 years of experience in data science, applied statistics, or analytics at a product-driven company

• Expert-level SQL and strong Python (pandas, scikit-learn, statsmodels); comfort moving from notebook to production code

• Track record of designing and interpreting controlled experiments, including resolving tricky issues like interference, novelty effects, and sample ratio mismatch

• Hands-on experience with causal inference and time-series forecasting methods

• MS or PhD in statistics, econometrics, computer science, or a related quantitative field (or equivalent practical experience)

• Strong communication skills - able to explain technical tradeoffs to non-technical stakeholders and change minds with data

• Bonus: experience with uplift modeling, bandits/reinforcement learning, Bayesian methods (PyMC, Stan), or building data science tooling for other teams