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 lendingDesign and build data pipelines and feature engineering frameworks to ensure high data quality and scalabilityTranslate complex data and model outputs into meaningful insights for strategic decision-makingCollaborate with cross-functional teams to identify opportunities for analytics-driven improvementsOwn the production, optimization, and automation of key analytics deliverables and reportingMonitor and assess the impact of business metrics and initiatives, providing recommendations for improvementStay 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 experienceStrong proficiency in SQL and Python (or R) for extracting, manipulating, and analyzing large datasetsHands-on experience building and deploying machine learning models using libraries such as scikit-learn, XGBoost, or TensorFlowSolid understanding of statistical methods and their application to business problemsDemonstrated ability to work independently and deliver results in a fast-paced environmentStrong communication skills to explain technical concepts to non-technical audiencesProven 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 operationsExperience with cloud-based data platforms and tools such as AWS, Azure, Databricks, or SnowflakeKnowledge of advanced machine learning techniques including ensemble methods, neural networks, or NLPExposure to Generative AI and LLMs such as GPT, RAG, or prompt engineering