Data Scientist – Responsible AI

Our vision for the future is based on the idea that transforming financial lives starts by giving our people the freedom to transform their own. We have a flexible work environment, and fluid career paths. We not only encourage but celebrate internal mobility. We also recognize the importance of purpose, well-being, and work-life balance. Within Empower and our communities, we work hard to create a welcoming and inclusive environment, and our associates dedicate thousands of hours to volunteering for causes that matter most to them.

Chart your own path and grow your career while helping more customers achieve financial freedom. Empower Yourself.

Applicants must be authorized to work for any employer in the U.S. We are unable to sponsor or take over sponsorship of an employment visa at this time, including CPT/OPT. The Data Scientist will support Empower’s Responsible AI strategy by developing, applying, and validating methods used to evaluate generative AI, agentic AI, and other AI-enabled business applications.

This role focuses on fairness, explainability, transparency, hallucination, safety, reliability, and related Responsible AI measures. The Data Scientist will work with AI/ML engineers, data engineers, security engineers, architects, governance partners, and business stakeholders to ensure evaluation methods are practical, repeatable, and aligned with business and regulatory needs.

• Apply statistical analysis and experimentation to evaluate generative AI, agentic AI, and other AI-enabled applications.
• Develop and maintain Responsible AI measures, benchmark datasets, test suites, and evaluation criteria.
• Evaluate AI systems for fairness, bias, explainability, transparency, hallucination, safety, reliability, and robustness.
• Use evaluation results to support the design, testing, selection, and ongoing assessment of AI-enabled business applications.
• Establish performance baselines and help identify changes in model or application behavior over time.
• Design and execute proofs of concept for new Responsible AI evaluation approaches.
• Assess emerging Responsible AI tools, frameworks, and methodologies for validity, limitations, and business applicability.
• Support the integration of Responsible AI testing into application development, quality assurance, and governance processes.
• Partner with engineering, architecture, quality assurance, governance, and business teams to align evaluation methods with use case requirements.
• Create and maintain model cards, evaluation reports, methodology documentation, and related guidance.
• Communicate findings, limitations, risks, and recommendations to technical and nontechnical stakeholders.
• Contribute to Responsible AI training, internal education, applied research, and industry engagement.
• Monitor emerging technologies and evaluation methods related to generative AI, agentic AI, and Responsible AI.

• Bachelor’s degree in Data Science, Computer Science, Statistics, Mathematics, Psychology, Economics, Operations Research, or another quantitative discipline.
• 2 to 5 years of experience in data science, statistical analysis, AI evaluation, applied research, or analytics.
• Strong Python programming skills and experience working with data analysis and AI evaluation tools.
• Strong SQL skills and experience working with large, complex datasets.
• Experience designing experiments, selecting appropriate measures, and interpreting statistical results.
• Experience evaluating generative AI applications, large language models, agentic systems, or other AI-enabled solutions.
• Knowledge of Responsible AI concepts, including fairness, bias, explainability, transparency, hallucination, safety, reliability, and robustness.
• Experience developing evaluation datasets, benchmarks, test cases, scorecards, or performance measures.
• Experience translating evaluation results into recommendations for business applications and technical teams.
• Experience with source control, automated testing, technical documentation, and basic software engineering practices.
• Ability to assess technical methodologies, identify limitations, and communicate findings clearly.
• Strong written and verbal…