Principal Data Scientist (Automotive)

TL;DR

Principal Data Scientist (Automotive): Building and optimizing predictive machine learning models for vehicle valuation with an accent on advanced feature engineering, cloud-based deployment, and end-to-end model lifecycle management. Focus on creating innovative data products that directly impact the automotive industry and collaborating with cross-functional teams to drive strategic data initiatives.

Location: Must be based in the USA or Canada

Salary: $130,000–$150,000 CAD / $155,000–$195,000 USD per year

Company

A global leader in data analytics and consumer intelligence, powering auto-related decisions through proprietary data and advanced industry expertise.

What you will do

• Develop and deploy machine learning models into production on AWS to solve complex automotive industry problems.
• Lead PI planning and strategic decision-making for data science initiatives with limited supervision.
• Collaborate with product and engineering teams to build and monitor scalable analytics pipelines.
• Create technical visual reports in Tableau for internal and external stakeholders.
• Document machine learning processes and communicate technical findings to non-technical audiences.
• Perform ad-hoc data analysis to support core product development.

Requirements

• Must be based in the USA or Canada.
• 8+ years of professional experience working with large datasets and statistical programming (Python/Spark).
• 8+ years of experience with database software (SQL, RedShift, Hive, MySQL).
• Master’s degree in Statistics, Data Science, Economics, or a related field.
• Proven ability to communicate complex technical processes to a lay audience.
• Strong presentation skills and experience creating technical documentation.

Nice to have

• PhD in Statistics, Data Science, Economics, or related field.
• Experience with automotive market data.
• Expertise in deploying cloud-based MLOps systems using AWS tools (SageMaker, Glue, Batch, Lambda).
• Experience deploying machine learning models into production with an API.

Culture & Benefits

• Work within a mature, tight-knit data science team.
• Opportunity to own the full-stack data science lifecycle from pipeline to production.
• Commitment to a diverse and inclusive workforce.
• Focus on innovation, collaboration, and data-driven decision-making.