About The Role
The Data Scientist will develop and productionize statistical and machine learning solutions for product, growth, and operational problems. The role spans exploratory analysis, experimentation, predictive modeling, and deployment, with direct ownership of translating messy behavioral and business data into reliable decisions.
The team works closely with product managers, software engineers, and data engineers to define metrics, design experiments, and ship models that improve customer experiences and business performance. This role is based in Chicago, IL and is fully remote.
Key Responsibilities
- Design and analyze A/B tests, quasi-experiments, and observational studies to measure product and business impact
- Build, validate, and deploy predictive models for forecasting, classification, ranking, segmentation, and anomaly detection using Python, pandas, scikit-learn, and SQL
- Develop reusable analytical datasets and feature pipelines with SQL, dbt, Spark, or similar data processing tools
- Partner with product and engineering teams to define success metrics, establish measurement frameworks, and convert analysis into shipped product improvements
- Present model findings, experiment results, and business recommendations clearly to technical and non-technical stakeholders
- Productionize models and analytical workflows through APIs, batch jobs, or cloud platforms such as AWS, GCP, or Azure
- Monitor model performance, data quality, drift, and experiment health; document assumptions, limitations, and reproducibility requirements
What We Are Looking For
- 3–8 years of experience in data science, applied statistics, machine learning, or a related role, including experience delivering analyses or models used in production
- Advanced Python and SQL skills, with hands-on experience using pandas, NumPy, scikit-learn, Jupyter, and version control with Git
- Strong foundation in statistics and experimentation, including hypothesis testing, confidence intervals, power analysis, causal inference, and metric design
- Experience building and evaluating machine learning models using appropriate validation strategies, performance metrics, and error analysis
- Proficiency with cloud data and analytics technologies such as Snowflake, BigQuery, Redshift, Databricks, Spark, dbt, or Airflow
- Bachelor’s or master’s degree in statistics, computer science, mathematics, economics, engineering, or a related quantitative discipline
- Bonus: Experience with deep learning, NLP or LLM applications, model serving, MLOps, causal modeling, or experimentation platforms