Principal Data Scientist

CarGurus•Boston, MA

12h•Hybrid

About The Position

As a core member of the Data Science team, the Principal Data Scientist will lead the design and development of high-impact machine learning systems that power CarGurus’ core products and strategic initiatives. You will own models and data pipelines end-to-end from problem framing and experimentation through production deployment, monitoring, and continuous improvements. Potential areas of support and ownership include Recommendations, Search Ranking, Instant Market Value algorithms, and new ML-driven product capabilities.

Requirements

7+ years of experience in Data Science or Machine Learning roles, with a consistent track record of shipping, owning, and iteratively improving production ML systems that drive material business impact.
Deep expertise in Machine Learning techniques for supervised and unsupervised learning across structured and unstructured datasets. Comprehensive knowledge of, and real-world experience with, measurement, evaluation, and testing of models.
Proven experience deploying and maintaining machine learning services in production, ideally in a cloud environment (e.g. AWS, SageMaker, Snowflake).
High proficiency in Python (or a similar languages widely used in the data science community) and SQL, plus experience establishing coding, testing, and reproducibility standards (e.g., shared libraries, experiment tracking, templates).
Ability to communicate technical details, trade‑offs, and analytical findings to audiences ranging from engineers to senior business leaders, using clear narratives and data‑driven recommendations.
Advanced degree (or proven experience) in Computer Science, Data Science, Mathematics, or any quantitative science which makes use of advanced data analytics or statistical or machine learning techniques

Responsibilities

Own the end‑to‑end design and implementation of production ML models and systems, including model architecture, feature strategy, and evaluation methodology.
In partnership with Data and Analytics teams, develop and maintain data pipelines to supply training and inference data for models, using SQL and Snowflake.
Collaborate with engineering leaders on system and API design to ensure ML solutions meet requirements for latency, reliability, observability, and maintainability in production.
Apply best practices for experimentation and model evaluation, including offline metrics, A/B testing design, and post‑launch analysis; coach other data scientists in applying these practices rigorously.
Communicate solutions to stakeholders through written documentation, demos and presentations, and data visualizations tailored to both technical and non‑technical audiences.