Data Scientist

Data ScientistThe 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 marketing performance measurement 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 casesDesign and implement econometric and causal inference models to quantify the impact of vehicle incentives, pricing, and commercial levers on sales, margin, and demandEstimate and interpret price and incentive elasticities across brands, segments, and regions, informing pricing and go-to-market strategiesDevelop defensible, well-documented methodologies that stand up to executive scrutiny and support strategic decision-makingCommunicate complex results clearly to both technical and non-technical audiencesPartner with Data Engineers to define and source relevant data features for modeling as well as drive adoption and a deep understanding of proper data usageDevelop and validate predictive models using techniques such as regression, random forests, gradient boosting, causal modeling and neural networksCommunicate findings and recommendations to non-technical audiences through clear visualizations and storytellingContribute to the maintenance of models in production environments, ensuring scalability and performanceConduct peer code reviews and support best practices in model development and deploymentCollaborate with both external and internal resources to support business requirements and key KPI measurementBasic Qualifications:Bachelor's degree in a quantitative discipline (e.g., Statistics, Economics or other quantitative field)Minimum of 5 years of experience in data science, econometrics or a related fieldProficiency in Python and SQLHands-on experience with big data and cloud platforms such as Databricks, Snowflake or SparkExposure to MLOps best practices, including model versioning, monitoring, and deployment pipelinesStrong grasp of machine learning algorithms like:Regression (linear, logistic)Causal Inference Models (Difference-in Difference, Regression Discontinuity Design)Experience with experimental design, and statistical inferenceAbility to translate complex data into actionable insights for business stakeholdersPreferred Qualifications:Master's degree in a quantitative discipline (e.g., Statistics, Economics or other quantitative field)Automotive experienceTree-based models (Random Forest, XGBoost, LightGBM)Clustering and dimensionality reduction (e.g., LDA, PCA, Dynamic Time Warping)Experience using PySpark for distributed data processing and feature engineeringExperience with Power BI or similar tools for data visualization and dashboarding2+ years of experience working with finance / pricing / incentives data2+ years of experience working with sales / commercial dataStrong communication and storytelling skills with the ability to influence decision-makersUnderstanding of CI/CD workflows for automating model testing and deploymentExperience working with real-time data pipelines and event-driven architectures