General Summary
Elevate is a technology firm which develops next-generation financial products focused on managing life's everyday expenses. The Data Science team conceptualizes, develops, deploys, and maintains predictive models using advanced statistical and machine learning methods. These models are used in Elevate's Underwriting, Account Management, and Operations applications. Additional responsibilities include developing and implementation of complex analysis to drive business decisions for our organization. The Data Scientist II will have a higher focus on technical ability and out of the box thinking.
Principal Duties and Responsibilities
- Led end‑to‑end model development, applying non‑linear, and advanced ML algorithms to design, test, and deploy models across underwriting, customer management, marketing, and operations.
- Apply advanced credit risk analytics to minimize credit and fraud losses, improve approval rates, and enhance product profitability
- Develop and deploy advanced ML models (tree‑based, ensemble, graph‑based / GraphML) for fraud, anomaly detection, and network risk analysis
- Productionize models in partnership with platform and engineering teams, supporting Docker‑based containerization and CI/CD‑aligned deployment workflows in Azure to ensure scalability, monitoring, and maintainability.
- Provide knowledge and insight on the third-party data providers such as Transunion, Clarity/Experian and Equifax to include knowledge of products and data available, effective use of variables, data dictionaries as well as advantages and limitations;
- Interact with business partners to support the needs and goals of all Elevate portfolios, Rock teams, and Pods.
- Present analytical findings, model results, and strategic recommendations to technical and non‑technical stakeholders with clarity and confidence.
Preferred/Nice-to-Have Responsibilities
- Developed and deployed AI proof‑of‑concepts using RAG and agentic AI architectures to support credit analytics, automated documentation, and analyst efficiency improvements.
- Led Snowpark‑driven modeling initiatives, enabling in‑database machine learning for credit risk, fraud, and customer analytics while ensuring scalability and compliance.
Experience and Education
- Minimum M.S./M.A. in a highly quantitative field (Computer Science, Statistics, Economics, Mathematics, Business or other quantitatively oriented degree) required. Doctoral Degree is a plus.
- At least 5 years of experience in Data Science, Risk or Modeling for consumer lending; Professional experience waived with Ph.D. Degree in highly quantitative field
- Demonstrated proficiency with advanced statistical modeling and substantial experience with machine learning techniques and graph-based ML in Risk/Fraud domain.
- Proficiency with Python and Azure is required
- Proficiency with extracting and manipulating data using multiple database technologies such as Snowflake.
- Good communication skills for communication with Risk Management peers
- Experience in financial services and/or Credit Risk Management preferred
- Applied experience in developing and productionizing Agentic AI solutions in enterprise settings
Collaborate closely with business and technical stakeholders to align modeling solutions with strategic goals, ensuring clear communication of insights, assumptions, and outcomes
