Equifax is where you can power your possible. If you want to achieve your true potential, chart new paths, develop new skills, collaborate with bright minds, and make a meaningful impact, we want to hear from you.
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 will do- 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 experience you will need- 5 - 7 years of professional experience as a data scientist or statistical modeler.
- Credit risk model experience
- 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.
What could set you apart - Master's Degree or PhD in Data Science, Computer Science, Mathematics, Engineering, Operations Research, Statistics or other related discipline.
- Experience as a Data Scientist in the banking industry.
- Very strong written and verbal communication skills with experience influencing stakeholders.
- Proven ability to quickly grasp complex concepts and identify potential model issues or validation gaps.
- Strong analytical skill, critical thinking and problem solving capabilities.
- Working knowledge of Google cloud and any other cloud environment.
- Experience in the UNIX environment is preferred.
- Ability to work independently and with minimal guidance.
#LI-Hybrid
#LI-AM2
We offer comprehensive compensation and healthcare packages, 401k matching, paid time off, and organizational growth potential through our online learning platform with guided career tracks.
Are you ready to power your possible? Apply today, and get started on a path toward an exciting new career at Equifax, where you can make a difference!
Primary Location:USA-Atlanta JV White
USA-Atlanta-One-Atlantic-Center
Function:Function - Data and Analytics
Schedule:Full time
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