Assistant Director, Data Science

Liberty Mutual Insurance•Boston, MA

4d•Hybrid

About The Position

At Liberty Mutual, the Insights & Solutions group uses data, analytics, and technology to deliver innovative solutions that drive our US Retail Markets business forward. Within it, the Claims Data Science team focuses on developing sophisticated AI/ML driven solutions to help create the most accurate, caring, and efficient claims organization in the insurance industry. Claims data science is bursting with opportunity. Recent advances in Large Language Models, Computer Vision, and other technologies bring many previously impracticable business challenges into the realm of possibility for data scientists. Claims data science can be a key competitive advantage for Liberty Mutual in the years to come; help us build that competitive advantage! 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.

Requirements

Broad knowledge of predictive analytic techniques and statistical diagnostics of models.
Expert knowledge of predictive toolset; reflects as expert resource for tool development.
Demonstrated ability to exchange ideas and convey complex information clearly and concisely.
Networks with key contacts outside own area of expertise. Ability to establish and build relationships within the aligned functional area or SBU.
Ability to give effective training and presentations to peers, management and less senior 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.

Nice To Haves

Broad conceptual understanding and practical knowledge of the end-to-end data science lifecycle.
Exceptional hands-on data science technical skills (e.g. SQL, Python, and Statistical Inference).
Experience collaborating with non-technical stakeholders to understand which problems need solving, design solutions, and bring them to market.
Experience working with complex Type II data to assemble training datasets to appropriately model operational processes.
Proficiency in Python and MLOps practices, with experience in version control (Git), code review, collaborative development workflows (e.g., GitHub/GitLab), and model versioning/experiment tracking (e.g., MLflow).
Knowledge of claims handling processes and experience working with claims data.
Experience developing LLM-based solutions for production use cases.
Practical experience with cloud platforms like AWS (preferably), Google Cloud, or Azure.
Familiarity with data pipeline and workflow management tools like Airflow, among others.

Responsibilities

Apply knowledge of sophisticated analytics techniques to manipulate large structured and unstructured data sets to generate insights to inform business decisions.
Lead end-to-end development of new predictive models for high-impact business outcomes (e.g., improving claims handling efficiency): frame and test hypotheses, design statistically rigorous experiments, assemble/label training data, engineer features, and train/validate models.
Build state-of-the-art ML systems that leverage structured data, unstructured text, and generative AI.
Select and implement appropriate algorithms and evaluation methods to deliver measurable accuracy and business value.
Follow ML Ops best practices to create organized code repos, production-quality code, and reproducible results.
Stay up-to-date with the latest advancements in data science and machine learning, and apply them to solving complex problems in the insurance claims domain.
Provide technical mentorship and guidance to junior data scientists.
Responsible for larger components of projects of moderate to high complexity.
Communicate findings through technical presentations, reports, and recommendations to both technical and non-technical stakeholders.
Participate in cross-functional working groups and contribute to the broader data science community to promote best practices.