Role overview
Equifax is looking for a Data Scientist, Model Risk Management to join our world-class Model Risk Management team. In this exciting role, you will have the opportunity to use cutting-edge cloud technology to conduct model validation across a wide range of Risk, Fraud, and Generative AI applications, while ensuring high-quality solutions and communicating critical results with partners across the organization.
- Equifax has a hybrid work schedule that allows for 2 days of remote work (Monday and Friday), with 3 days onsite (Tuesday, Wednesday, Thursday) every week.
- This role reports to our office in Alpharetta, Georgia OR midtown ATL (OAC).
- This position does not offer immigration sponsorship (current or future) including F-1 STEM OPT extension support.
- *This is a direct-hire role and is not open to C2C or vendors.
What you'll work on
- Conduct model validation to verify quality of models and identify data anomalies using statistical and advanced machine learning approaches.
- Apply critical rigor and assess models built for various verticals including retail banking, auto lending, communications and utilities, mortgage, and payments. Experience in the credit risk analytics domain is preferred.
- Research new and advanced data science techniques as appropriate for a specific solution.
- Be able to learn quickly and conduct research and exploration independently on new modeling techniques; Keep him/herself up to date to the latest in the industry.
- Must be proficient in Python and SQL and have sufficient knowledge in such advanced computational languages/applications as Big Query, TensorFlow, Scala, SAS, and/or R.
- Be able to communicate concisely in both written and oral English and to create model validation reports, including presentation to senior management, supervisory authorities, and model stakeholders.
- Must be a great team player.
- Must be self-motivated, disciplined and task focused; Be able to deliver high quality results within strict deadlines.
What we're looking for
- 5 - 7 years of professional experience as a data scientist or statistical modeler in at least one of the following: identity and fraud, credit risk, telecommunications, financial services, payment, ecommerce, B2B or B2C, marketing, insurance, or security analytics arena.
- Bachelor’s degree or higher in Data Science, Computer Science, Mathematics, Engineering, Operations Research, Statistics or other related discipline; Master’s or PhD highly preferred.
- Strong quantitative analytical experience, including hands-on and background in using statistics, regression modeling, neural networks, decision trees, random forest, support vector machine, kernel based methods, clustering and similar methods.
- Experience with advanced architecture including Deep Neural Networks, Generative AI, and Agentic AI frameworks is highly desirable.
Tags & focus areas
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