Data Scientist - AI

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. The role focuses on fairness, explainability, transparency, hallucination, safety, reliability, and related Responsible AI measures.

• 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 communication skills.
• Ability to work independently and collaborate across technical, governance, and business teams.

• Advanced degree in a quantitative, behavioral science, computer science, AI, or Responsible AI-related discipline.
• Experience evaluating large language models, generative AI applications, retrieval-augmented generation systems, or agentic AI solutions.
• Experience with model cards, system cards, AI evaluation frameworks, or AI assurance documentation.
• Familiarity with Responsible AI standards and frameworks, including NIST or ISO guidance.
• Experience with fairness metrics, explainability techniques, hallucination and grounding evaluation, human evaluation, LLM-as-judge methods, or adversarial testing.
• Experience applying Responsible AI evaluation methods within production or business-facing applications.
• Experience in financial services or another regulated industry.
• Experience contributing to technical publications, conferences, research collaborations, or industry working groups.

• Medical, dental, vision, and life insurance.
• Retire…