• Lead the design, development, and deployment of scalable machine learning models to optimize vehicle sales and demand management • Identify business opportunities and collaborate with stakeholders to translate complex business requirements into machine learning solutions that drive growth and impact • Work closely with engineering and product teams to deliver data science services and integrate them into the Auto1 tech infrastructure • Monitor and improve model performance in production, communicate results to stakeholders, and define actionable next steps • Mentor and support junior Data Scientists, set technical direction, and ensure best practices

• Design, develop, and deploy machine learning models to solve complex business problems in AdTech • Analyze large datasets to generate actionable insights • Build and maintain scalable data pipelines using big data tools • Perform data preprocessing, cleaning, feature engineering, and model evaluation • Collaborate with cross-functional teams including engineers, analysts, and product managers • Ensure model accuracy, reliability, and scalability through rigorous testing and validation • Stay updated on emerging data science techniques, tools, and best practices • Contribute to team discussions, process improvements, and knowledge sharing

• Drive signal detection and early alerting by applying new technologies to reduce false-positive rates and increase confidence in results through reproducible analysis approaches and controls. • Standardize signal review outputs (evidence summaries, trend context, case series characterizations) and ensure consistent governance across multiple data sources. • Maintain ongoing tracking and documentation of the signal lifecycle so that conclusions, decisions, and actions remain transparent and traceable. • Continuously improve the quality of pharmacovigilance data (completeness, consistency, de-duplication, coding, timeliness) and deliver "easy-to-digest" views (dashboards, scorecards, automated reports) to increase transparency of safety trends and data reliability. • Provide decision-ready insights to management via regular updates and ad-hoc escalations, with summaries focused on what has changed, why it matters, and what comes next; contribute to structured quarterly reviews for key products. • Partner with PV leadership and digital/automation teams to advance pharmacovigilance technologies and signal management capabilities — translate needs into clear requirements and acceptance criteria, support testing and implementation (training, feedback loops), and ensure continuity with minimal disruption.

• Design, develop, and deploy advanced data science solutions focused on time series modeling and optimization problems. • Analyze large and complex datasets to extract actionable insights that support business decision-making. • Build, validate, and maintain predictive and optimization models using Python and SQL. • Work closely with clients and internal stakeholders to understand requirements and present data-driven recommendations. • Collaborate with cross-functional teams, including data engineers and product teams, to ensure end-to-end solution delivery. • Contribute to best practices in modeling, experimentation, and analytical workflows. • Communicate findings clearly to both technical and non-technical audiences.