About the job
About Us
At CarGurus (NASDAQ: CARG), we are dedicated to empowering individuals on their journeys. Our story began with a small group of innovators committed to enhancing trust and transparency in car shopping. Over the years, we have evolved into the largest and fastest-growing automotive marketplace, enjoying over 15 years of profitability and continuous innovation.
Our Mission
The automotive landscape is changing, and so are we. We're transforming the car buying experience by moving it online, assisting our customers throughout their entire journey—from selling their old vehicles to financing, purchasing, and delivering new ones. Each month, millions of consumers engage with CarGurus.com, supported by around 30,000 dealerships. Our employees thrive in a people-first culture that promotes kindness, collaboration, and innovation. We believe that disrupting a trillion-dollar industry requires diverse perspectives. Join us on this exciting journey!
Position Overview
The Data Science Manager will helm our Inventory & Dealer Data Science team, focusing on the development, deployment, and optimization of machine learning models that drive CarGurus’ products and insights. This team is crucial in modeling marketplace dynamics and dealer behaviors and is accountable for the complete ML lifecycle, from research and development to production. As a leader, you will set the strategic direction for the team, ensuring impactful outcomes while nurturing a culture of innovation and technical excellence. Collaboration with Product, Engineering, Analytics, and other stakeholders will be key in aligning the team’s efforts with CarGurus’ goals for intelligence-driven products.
Key Responsibilities
- Lead the design, training, and evaluation of machine learning models aimed at Inventory and Dealer intelligence, utilizing techniques such as recommendations, demand forecasting, churn risk prediction, and valuation algorithms.
- Oversee experimentation, A/B testing, and the assessment of production models to evaluate their business impact effectively.
- Build a high-performing team through regular coaching, feedback, and performance evaluations; ensure equitable opportunities for growth and development.
- Promote innovation, continuous improvement, and the adoption of best-in-class ML and AI practices across the organization.

