Director I, Data Science, People, Purpose & Brand
L
Liberty MutualData Science
Telecommuting up to 60% when near an office; 100% remote supported otherwise.Full-TimeDirector
Salary not disclosed
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Job Details
- Experience
- Ph.D. degree and a minimum of 3 years of relevant experience, a Master's degree and a minimum of 6 years of relevant experience, or a Bachelor's degree and a minimum of 8 years of relevant experience.
- Required Skills
- PythonSQLCloud ComputingGitMachine LearningData scienceRESTful APIsGenerative AI
Requirements
- Strong foundation in Data Science principles including Probability, Statistics, and AI/ML.
- Experience with LLMs, embeddings, and generative/agentic systems.
- Proficient in Python with experience writing production-quality code.
- Strong SQL skills for querying, validation, and data exploration.
- Experience with APIs and integrating external model or vendor services.
- Familiarity with cloud computing concepts and services.
- Experience evaluating models for fairness, bias, privacy, explainability, or responsible AI.
- Comfortable with Git-based version control and collaborative code review.
- Ph.D. in a scientific field with 3+ years experience, Master's with 6+ years, or Bachelor's with 8+ years experience.
Responsibilities
- Architect, develop, and maintain tooling and pipelines to support GenAI model development, evaluation, deployment, and monitoring.
- Design and operationalize scalable evaluation frameworks and metrics for GenAI systems to ensure quality, safety, and organizational alignment.
- Lead the vendor AI evaluation program by defining criteria, running benchmarks, and providing recommendations for selection and integration.
- Build reusable components leveraging APIs and templates that enable rapid iteration and reliable deployment of GenAI features.
- Partner with stakeholders to translate business needs into technical designs, evaluation plans, and implementation roadmaps.
- Promote strong engineering hygiene, including CI/CD, version control, testing, documentation, and reproducible pipelines.
- Provide informal mentorship for data science and analytics colleagues on tools and evaluation processes.
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