DescriptionThe Claims and Service AI Enablement team is hiring a Data Scientist to own Generative AI and machine learning support for Knowledge & Development Solutions (KDS). This role will partner with KDS stakeholders to design, build, evaluate, and scale AI/ML solutions across knowledge management, training, content delivery, and employee enablement.
KDS helps Liberty Mutual deliver the right content to the right audience at the right time through contextual knowledge, guided learning, learning experience design, training operations, and knowledge-centered support. This role will apply predictive modeling, Rapid experimentation & prototyping, GenAI, RAG, traditional ML, and evaluation to improve how employees find, learn, and use information.
**Candidates who live within 50 miles of Boston, MA; Portsmouth, NH; Seattle, WA; Columbus, OH; or Plano, TX will follow a hybrid schedule, coming into the office two days per week. Otherwise, this role is remote with occasional travel.**
Responsibilities:
- Own the Generative AI and ML portfolio for KDS in partnership with KDS leaders
- Identify, prioritize, and deliver AI/ML use cases that improve knowledge discovery, content effectiveness, learning outcomes, and employee enablement.
- Design, build, and evaluate predictive models, GenAI applications, semantic search, RAG solutions, and analytical tools.
- Apply full-stack data science practices from problem framing and data exploration through modeling, evaluation, deployment partnership, measurement, and optimization.
- Partner with KDS, Technology, Engineering, Architecture, UX, Compliance, and business stakeholders to move solutions from concept to production-minded implementation.
- Define and implement measurement strategies to assess model performance, adoption, operational efficiency, content quality, training impact, and business outcomes.
- Provide technical mentorship and thought leadership on AI/ML solution design, evaluation, experimentation, and scalable implementation patterns.
Preferred Skills and Experiences:
- Full-stack data science experience, including problem framing, data exploration, modeling, evaluation, deployment partnership, measurement, and optimization.
- Expert-level Python development with strong object-oriented design, modular architecture, and engineering best practices.
- Proven experience building machine learning, predictive modeling, statistical, Generative AI, or data science solutions that solve real business problems.
- Strong understanding of supervised and unsupervised learning ML, experimentation, and evaluation.
- Experience with GenAI, LLM-based applications, Q&A, chat, embeddings, prompt engineering, retrieval-augmented generation, or AI-assisted workflows.
- Experience working with structured and unstructured data, including operational data, behavioral data, documents, knowledge articles, training materials, communications, or other enterprise content.
- Strong ML Ops experience, including experimentation, model lifecycle management, monitoring, observability, governance, and production readiness.
- Ability to partner with non-technical stakeholders, understand complex workflows, and translate ambiguous needs into practical, measurable AI/ML solutions.
- Strong communication skills with the ability to explain technical concepts clearly to Product, business, engineering, and leadership audiences
Qualifications
- Broad knowledge of predictive analytic techniques and statistical diagnostics of models.
- Advanced knowledge of predictive toolset; reflects as expert resource for tool development.
- Demonstrated ability to exchange ideas and convey complex information clearly and concisely.
- Ability to establish and build relationships within and outside the organization.
- Ability to give effective training and presentations to management and other groups.
- Ability to use results of analysis to persuade team, department management or senior management to a particular course of action.
- Broad knowledge of business drivers and market context.
- Has a value driven perspective with regard to understanding of work context and impact.
- Competencies typically acquired through a Ph.D. degree (in Statistics, Mathematics, Economics, Actuarial Science or other scientific field of study) and a minimum of 3 years of relevant experience, a Master`s degree (scientific field of study) and a minimum of 6 years of relevant experience or may be acquired through a Bachelor`s degree (scientific field of study) and a minimum of 8 years of relevant experience.
