Assistant Director, Data Science (STP)

Description

This a range posting. Candidates will be considered for the appropriate level depending upon experience and qualifications.

 

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.

 

The US Retail Markets Data Science team brings together a diverse range of talent to predict future risk and what our customers will need to recover. Our data engineers write code that turns trillions of bits of information into structured data—data that our hundred-plus Data Scientists analyze with cutting-edge modeling techniques to unlock insights. From there, our tools and deployment teams ensure this data can be practically applied to business problems across US Retail Markets. Join us and be a part of this dynamic group driving industry-leading data segmentation, fueling the team’s success now and into the future. 

 

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!

 

The US Retail Markets Claims Data Science team is hiring three Assistant Director and/or Director, Data Science positions as part of a broader expansion of our team. These are individual contributor positions, two roles will focus on Casualty claims, and one will focus on Property claims.

 

**This role may have in-office requirements based on candidate location**

**Level of position offered will be based on skills and experience at manager discretion**

 

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. 

 

Preferred skills and experience:

• 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).

 

Additional skills and experiences that are nice to have:

• 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.