• Evaluate and understand current & nascent modelling methods in the Data Science space • Maintain, expand, and ensure the data quality of natural language • Train and evaluate Large Language Models on a variety of topics • Work closely with business stakeholders to deliver new Natural Language Processing & Artificial Intelligence products • Develop new features for existing algorithms through statistical and machine learning methodologies • Validate and implement modeling improvements to increase accuracy of data science Algorithms • Embrace an R&D mindset through Rapid Prototyping & Iterative Experimentation • Work closely with Tech delivery teams to productionize successful models in an on-demand environment

• Develop AI solutions: Create ML and generative AI systems for RAG pipelines, chatbots, classification, forecasting, and recommendation. • Ensure alignment with enterprise standards, seamless integration, and secure scalability. • End ‑ to ‑ End Solution Development: Own the AI lifecycle from problem framing through deployment: data prep, modeling, evaluation, model change management, orchestration , observability , drift detection, and synthetic data generation. • Collaborate closely with AI engineers, data engineers, platform, security, and IT to ensure solutions are robust, maintainable , production ready, follow safety filters/guardrails, and rollback plans. • Drive execution from discovery to rollout by defining scope, milestones, and acceptance criteria; managing dependencies/risks; coordinating cross-functional workstreams; and maintaining clear status reporting, issue escalation, and delivery timelines. • Partners closely with Product, Underwriting, Distribution, Risk, Legal, and Compliance to align AI initiatives with enterprise objectives and governance expectations. • Translates complex model behavior and evaluation outcomes into clear, actionable business insights with defined success criteria (accuracy, cost, performance, reuse, ROI).

• Build and optimize ML, deep learning, and GenAI models using modern frameworks (PyTorch, TensorFlow, scikit-learn) • Design and deliver RAG pipelines, hybrid retrieval systems, and vector-based search workflows • Develop Agentic AI solutions including tool orchestration, reasoning flows, and safe-execution strategies • Run structured experimentation using evaluation metrics (BERTScore, BLEURT, semantic similarity, retrieval precision/recall) • Integrate solutions into GCP (Vertex AI, Workbench, Vector Search) and AWS (SageMaker, Bedrock) • Build ingestion, enrichment, and semantic retrieval flows for high-quality knowledge and feature engineering • Partner with onshore and offshore DS/ML engineers to ensure quality, consistency, and shared technical patterns

Role Description As part of the Valuation Services division of JD Power, you will work to understand what features drive a vehicle's price and predict future values. Our data science team is accountable for every data point we output and provides industry-leading, accurate, transparent analytics. We are looking for a Data Scientist who will be free to utilize the latest technologies to solve challenging problems, create innovative machine learning processes from the ground up, deploy them into production on our AWS server, and develop the tools for monitoring them. We are a mature and tight-knit data science team looking to add another top-shelf team member ready to make a difference in the automotive industry. Join us! Impact You Will Have in This Role - Every new data product we output will solve a problem in the auto industry. - Every Data Scientist will build models that go into production and directly impact our core products. What You’ll Be Doing in This Role - Daily immersion in automotive industry data. - Involved in strategic planning and decision-making for data science initiatives. - Independently create decks to make highly technical predictions relatable to the automotive industry. - Documentation of machine learning processes. - Deep expertise in advanced feature engineering and AI/ML model building. - Expert in SQL, Python/R, Advanced Excel, AWS SageMaker, Palantir Data Science Integrations, or other Machine Learning frameworks. - Collaborate closely with technology and engineering teams to improve existing processes and build out new ones. - Work with the product team to develop the analytics that will power new products. - Leads PI (Program Increment) planning for activities to be conducted in the next quarter, with limited supervision, for major developmental modules or projects. - Create visual reports in Tableau for internal and external clients that summarize findings. - Team environment that encourages the sharing of ideas to build the best predictive algorithms. - Ad-hoc data analysis and reporting. Qualifications - Ability to communicate technical processes to a lay audience. - Presentation experience. - Curiosity and passion for solving problems with data. - 8+ years of professional experience working with large datasets. - 8+ years of professional experience with statistical programming software (Spark, Python preferred). - 8+ years of professional experience with database software (RedShift, Hive, SQL, MYSQL). - Master’s Degree in Statistics, Data Science, Economics, or a related field. Nice to Have - Experience with automotive market data. - Professional expertise deploying cloud-based ML Ops systems. - Experience working with AWS tools (Sagemaker, Glue, Batch, Lambda…). - Some shareable examples of data visualizations you have created. - Experience deploying machine learning models into production with an API. - PhD in Statistics, Data Science, Economics, or related field. Salary This position has a starting salary range of $130K - $150K CAD / $155K - $195K USD per year. This is the range we reasonably and in good faith expect to pay for the role at the time of posting. An employee’s pay within the range is determined by a number of factors, including relevant skills, education, qualifications, experience, performance, business or organizational needs, and geographic location. Our Values - We POWER Our Customer’s Success. - We are Innovative, Collaborative and Grounded in Data. - We Make Things Easy. - We Get It Done. - We Start with Trust & Prove it Everyday.