Data Scientist - Commercial Analytics

The Commercial Analytics team is looking for a Data Scientist to join our team. Your mission is to build and scale trusted data science products that power commercial performance measurement and growth while promoting data science best practices, actionable recommendations and a high bar for model quality and reliability. Data scientists work closely with data engineers, analysts, and business teams to design analytics solutions, implement advanced algorithms and evaluate the performance of use cases. Ideal candidates are self-motivated, inquisitive and creative, with a strong desire to solve real-world problems using data. In this role, you will: Collaborate with business stakeholders to identify high-impact opportunities for statistical and machine learning use cases. Develop defensible, well-documented methodologies that stand up to executive scrutiny and support strategic decision-making. Communicate complex results clearly to both technical and non-technical audiences. Partner with data engineers to define and source relevant data features for modeling as well as drive adoption and a deep understanding of proper data usage. Develop and validate predictive models using techniques such as regression, random forests, gradient boosting, causal modeling and neural networks. Communicate findings and recommendations to non-technical audiences through clear visualizations and storytelling. Contribute to the maintenance of models in production environments, ensuring scalability and performance. Conduct peer code reviews and support best practices in model development and deployment. Collaborate with both external and internal resources to support business requirements and key KPI measurement

Requirements:

Basic Qualifications:

Bachelor's degree in a quantitative discipline (e.g., Statistics, Economics, Computer Science or other quantitative field) Minimum of 3 years of experience in data science, econometrics or a related field Proficiency in Python and SQL Hands-on experience with big data and cloud platforms such as Databricks, Snowflake or Spark Exposure to MLOps best practices, including model versioning, monitoring, and deployment pipelines Strong grasp of machine learning algorithms like: Regression (linear, logistic) Causal Inference Models (Difference-in Difference, Regression Discontinuity Design) Tree-based models (Random Forest, XGBoost, LightGBM) Neural networks Clustering and dimensionality reduction (e.g., LDA, PCA, Dynamic Time Warping) Ability to translate complex data into actionable insights for business stakeholders

Preferred Qualifications:

Master's degree in a quantitative discipline (e.g., Statistics, Economics, Computer Science or other quantitative field) Automotive experience 2+ years of experience working with commercial data Experience using PySpark for distributed data processing and feature engineering Strong communication and storytelling skills with the ability to influence decision-makers Understanding of CI/CD workflows for automating model testing and deployment Experience working with real-time data pipelines and event-driven architectures Experience with experimental design, and statistical inference Exposure to feature stores and model registries in MLOps environments Experience with Power BI or similar tools for data visualization and dashboarding