Data Scientist

Scale.jobs • Full-time • Remote (Chicago, IL, US) • 3d ago

About The Role

The role drives product innovation and business strategy through rigorous statistical analysis, experimental design, and predictive modeling. This position bridges the gap between raw product data and actionable strategy, turning complex behavioral patterns into clear algorithmic solutions and product decisions.

The team works collaboratively with product managers, data engineers, and software developers to design robust A/B tests, build scalable machine learning prototypes, and establish foundational metrics that define product success.

Key Responsibilities

Design, execute, and analyze rigorous A/B tests and multivariate experiments to evaluate product feature launches and user experience changes
Develop and deploy predictive models, including user churn forecasting, lifetime value estimation, and recommendation algorithms, using Python and SQL
Build and maintain robust, scalable data pipelines using PySpark, dbt, and SQL to process terabytes of user engagement data
Establish key performance metrics and build automated, interactive dashboards in Tableau or Looker to democratize data insights across the organization
Perform deep-dive exploratory data analyses to identify friction points in the user journey and present actionable recommendations to executive stakeholders
Collaborate with machine learning engineers to integrate prototype models into reliable production environments

What We Are Looking For

3–6 years of experience as a Data Scientist, with a proven track record of driving product decisions through quantitative analysis
Advanced proficiency in Python (pandas, NumPy, scikit-learn, statsmodels) and writing highly optimized SQL queries on cloud data warehouses
Strong foundation in statistical theory, including hypothesis testing, experimental design, causal inference, and regression modeling
Experience working with modern distributed computing frameworks like Apache Spark or Databricks
Bachelor's or Master's degree in a highly quantitative field such as Statistics, Mathematics, Computer Science, Economics, or equivalent experience
Bonus: Experience with MLOps tools (MLflow, SageMaker), containerization (Docker), or version control workflow using Git