We have an exciting opportunity for you to accelerate your data science career in Auto Finance and shape the future of lending.
As a Data Scientist Associate in the Auto Data and Analytics team, you deliver advanced analytics and predictive modeling to drive data-driven decision-making across the auto lending business. You collaborate with partners to generate actionable insights that directly impact business outcomes and reach senior leadership.
Job responsibilities
- Develop and deploy machine learning models and analytics solutions to solve business problems in auto lending
- Design and build data pipelines and feature engineering frameworks to ensure high data quality and scalability
- Translate complex data and model outputs into meaningful insights for strategic decision-making
- Collaborate with cross-functional teams to identify opportunities for analytics-driven improvements
- Own the production, optimization, and automation of key analytics deliverables and reporting
- Monitor and assess the impact of business metrics and initiatives, providing recommendations for improvement
- Stay current with emerging data science techniques, tools, and industry trends to drive innovation
Required qualifications, capabilities, and skills
- Formal training or certification in data science or analytics and four years applied experience
- Strong proficiency in SQL and Python (or R) for extracting, manipulating, and analyzing large datasets
- Hands-on experience building and deploying machine learning models using libraries such as scikit-learn, XGBoost, or TensorFlow
- Solid understanding of statistical methods and their application to business problems
- Demonstrated ability to work independently and deliver results in a fast-paced environment
- Strong communication skills to explain technical concepts to non-technical audiences
- Proven track record of translating business problems into analytical solutions and actionable insights
Preferred qualifications, capabilities, and skills
- Industry experience in auto lending, consumer finance, credit risk, marketing analytics, or operations
- Experience with cloud-based data platforms and tools such as AWS, Azure, Databricks, or Snowflake
- Knowledge of advanced machine learning techniques including ensemble methods, neural networks, or NLP
- Exposure to Generative AI and LLMs such as GPT, RAG, or prompt engineering
