Data Scientist

Senior Data Scientist, Automotive Residual Value Modeling

100% REMOTE

What You'll Do

Join Experian's data science team to develop and improve models that estimate automotive residual values over time. You'll work with data science, engineering, and business partners to turn vehicle and market data into reliable estimates that support customer and business decisions.

• Define modeling targets, prediction horizons, and evaluation criteria for automotive residual value use cases.
• Explore and prepare large, varied datasets, which may include vehicle specifications, transaction and market data, economic indicators, and other relevant sources.
• Build and evaluate predictive models using appropriate statistical and machine learning methods, such as regression, gradient boosting, and random forests.
• Design validation strategies that reflect how the models will be used, including time-based testing and analysis across vehicle segments, geographies, and market conditions.
• Assess model accuracy, bias, stability, and uncertainty; investigate errors and identify opportunities to improve performance.
• Develop interpretable outputs and explain model behavior, assumptions, and limitations to technical and business audiences.
• Work with data engineering and software teams to prepare models and data pipelines for production and help monitor performance as data and market conditions change.
• Measure the business value of model improvements and communicate results through clear documentation, presentations, and recommendations.

Basic Qualifications

• Master's degree or higher in data science, statistics, economics, computer science, engineering, or another quantitative field; or equivalent practical experience.
• 3+ years of experience developing predictive models or analytical solutions in a professional setting.
• Strong programming skills in Python and SQL, including experience with common data science libraries such as pandas and scikit-learn.
• Experience selecting, training, and evaluating statistical or machine learning models for structured data.
• Understanding of sound validation practices, including holdout testing, time-based validation, and methods to reduce data leakage.
• Experience working with large datasets using Spark or similar distributed processing tools.
• Ability to explain technical findings and model limitations clearly to both technical and nontechnical partners.

Preferred Qualifications

• Experience with automotive, vehicle valuation, lending, insurance, leasing, auctions, or other asset-value modeling.
• Experience forecasting values or depreciation across multiple time horizons and market conditions.
• Experience with model interpretability, prediction intervals or other uncertainty measures, and ongoing model monitoring.
• Experience building or supporting production data science workflows in a cloud or distributed computing environment.
• Familiarity with deep learning frameworks such as PyTorch or TensorFlow, when appropriate to the data and use case.
• Experience using text or other unstructured data in predictive modeling.
• Familiarity with generative AI tools, when relevant to extracting or organizing information from unstructured sources.

Required Skills : Senior Data Scientist, Automotive Residual Value Modeling 100% REMOTE What You'll Do Join Experian's data science team to develop and improve models that estimate automotive residual values over time. You'll work with data science, engineering, and business partners to turn vehicle and market data into reliable estimates that support customer and business decisions. Define modeling targets, prediction horizons, and evaluation criteria for automotive residual value use cases. Explore and prepare large, varied datasets, which may include vehicle specifications, transaction and market data, economic indicators, and other relevant sources. Build and evaluate predictive models using appropriate statistical and machine learning methods, such as regression, gradient boosting, and random forests. Design validation strategies that reflect how the models will be used, including time-based testing and analysis across vehicle segments, geographies, and market conditions. Assess model accuracy, bias, stability, and uncertainty; investigate errors and identify opportunities to improve performance. Develop interpretable outputs and explain model behavior, assumptions, and limitations to technical and business audiences. Work with data engineering and software teams to prepare models and data pipelines for production and help monitor performance as data and market conditions change. Measure the business value of model improvements and communicate results through clear documentation, presentations, and recommendations. Basic Qualifications Master's degree or higher in data science, statistics, economics, computer science, engineering, or another quantitative field; or equivalent practical experience. 3+ years of experience developing predictive models or analytical solutions in a professional setting. Strong programming skills in Python and SQL, including experience with common data science libraries such as pandas and scikit-learn. Experience selecting, training, and evaluating statistical or machine learning models for structured data. Understanding of sound validation practices, including holdout testing, time-based validation, and methods to reduce data leakage. Experience working with large datasets using Spark or similar distributed processing tools. Ability to explain technical findings and model limitations clearly to both technical and nontechnical partners. Preferred Qualifications Experience with automotive, vehicle valuation, lending, insurance, leasing, auctions, or other asset-value modeling. Experience forecasting values or depreciation across multiple time horizons and market conditions. Experience with model interpretability, prediction intervals or other uncertainty measures, and ongoing model monitoring. Experience building or supporting production data science workflows in a cloud or distributed computing environment. Familiarity with deep learning frameworks such as PyTorch or TensorFlow, when appropriate to the data and use case. Experience using text or other unstructured data in predictive modeling. Familiarity with generative AI tools, when relevant to extracting or organizing information from unstructured sources.

Basic Qualification :

Additional Skills :


Long Term Project

Background Check : Yes


Drug Screen : Yes


Notes :
Selling points for candidate :Long Term Project
Project Verification Info :"The information provided below is for Client Systems AV use only and is not to be distributed publicly, or to any third party. Any distribution of the below information will result in corrective action from Client Systems Vendor Management. MSA: Restricted Client Letter: Will Provide"
Exclusive to Client :No
Face to face interview required :No
Candidate must be local :No
Candidate must be authorized to work without sponsorship ::No
Interview times set :Yes
Type of project :
Master Job Title :Data Scientist
Branch Code :