Assistant Director, Data Science

Join our team to fill the explosive demand for AI in the legal industry. The Global Legal & Compliance department at Liberty Mutual needs your help to meet these opportunities by applying data science tools including deep learning and generative AI to legal problems such as document analysis, billing audit, and outcome simulations.

We are looking for an experienced data scientist to build AI and decision-support tools that help the Legal Department work more efficiently and deliver better outcomes to our clients and policyholders on legal related issues. This will include end-to-end development from ideation to deployment and working directly with senior legal leaders to understand their business needs and demonstrate model results.

In this role, you will collaborate closely with a team of data scientists, legal professionals, analysts, and IT developers. You will independently work with stakeholders to drive project outcomes by applying your technical skills to high priority opportunities and enhance the Data Science team’s processes and capabilities. You and the team will work with clients across the company to develop a strong understanding of business needs. You will research and apply NLP, machine learning, generative AI, and statistical algorithms to projects aimed at reducing legal costs and enabling advanced capabilities for legal professionals at Liberty Mutual.

Example projects include using deep learning-based NLP models to classify millions of text-based legal records, building machine learning models to predict the outcome of a case, and using generative AI to summarize and extract key information from extensive documents to inform decision-making.

Responsibilities:

• Develops predictive and explanatory AI solutions that help improve legal decision-making using deep learning, machine learning, NLP (Natural Language Processing), and generative AI techniques.
• Applies broad knowledge of sophisticated analytics techniques to manipulate large structured and unstructured data sets to generate insights to inform business decisions.
• Identifies new strategic opportunities for use of theoretical methods and tools.
• Researches and develops predictive analytic tools.
• Leverages knowledge to create and design solutions for business needs.
• Mines large data sets using sophisticated analytical techniques to generate insights and inform business decisions.
• Identifies and tests hypotheses, ensuring statistical significance, and builds predictive models for business application.
• Translates quantitative analyses and findings into accessible visuals for non-technical audiences, providing a clear view into interpreting the data.
• Enables the business to make clear tradeoffs between and among choices, with a reasonable view into likely outcomes.
• Customizes analytic solutions to specific client needs.
• Responsible for larger components of projects of moderate to high complexity.
• Guides aspects of project design as a technical consultant for the team.
• Regularly engages with the data science community and participates in cross functional working groups.

• Broad knowledge of predictive analytic techniques and statistical diagnostics of models.
• Expert knowledge of predictive toolset and serves as expert resource for tool development.
• Demonstrated ability to exchange ideas and convey complex information clearly and concisely.
• Demonstrated proficiency in Python.
• Experience working with common technical infrastructure utilities (cloud-based compute, storage services, version control, etc).
• Ability to effectively self-manage longer-term efforts by establishing and adhering project milestones and deadlines.
• Networks with key contacts outside own area of expertise. Ability to establish and build relationships within the aligned functional area or business unit.
• Ability to give effective training and presentations to peers, management and business leaders.
• Ability to use results of analysis to persuade team or department management to a particular course of action.
• 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 2 years of relevant experience, a Master’s degree (scientific field of study) and a minimum of 4 years of relevant experience or may be acquired through a Bachelor’s degree (scientific field of study) and a minimum of 5+ years of relevant experience.

Preferred Qualifications:

• Experience developing and implementing generative AI solutions.
• Understanding of MLOps principles to aid in development and deployment of efficient, robust, and repeatable work products.
• Experience applying machine learning, deep learning, and/or NLP techniques, especially using PyTorch, Hugging Face, and sci-kit-learn on large, unstructured data.
• Experience working with insurance, claims, or legal data.