Analyst Data Science- LII Commercial and Specialty

About Liberty Mutual

At Liberty Mutual, we believe progress happens when people feel secure. By providing protection for the unexpected and delivering it with care, we help people embrace today and confidently pursue tomorrow.

In business since 1912, and headquartered in Boston, today we are the fifth largest global property and casualty insurer based on 2022 gross written premium. We also rank 86 on the Fortune 100 list of largest corporations in the US based on 2022 revenue. As of December 31, 2022, we had $50 billion in annual consolidated revenue.

We employ over 50,000 people in 29 countries and economies around the world. We offer a wide range of insurance products and services, including personal automobile, homeowners, specialty lines, reinsurance, commercial multiple-peril, workers compensation, commercial automobile, general liability, surety, and commercial property.

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Analyst Data Science- LII Commercial and Specialty

Location UK

Description

The Liberty Mutual, Global Risk Solutions, DSS is seeking a Senior Data Scientist within the Liberty International business team focusing on Specialty. This team is responsible for solving complex international speciality insurance problems with analytics insights,ML and AI models that deliver business value. The team is looking for a passionate individual who is full of curiosity, open to new ideas, and willing to fail fast and embrace a culture of experimentation. This individual will join our Agile team and play an essential role in building out in-house AI/ML capabilities and be involved in the end-to-end product cycle, from ideation to model development/deployment to field implementation. Typical projects would involve the following:

• Design and develop Machine learning models to aid AI insights for the UW , Claims and business development domains
• Use and fine-tune large language models to glean valuable insights from different kinds of unstructured data.
• Develop and adopt the best-in-class MLOps best practices throughout the data science lifecycle

This level reflects solid knowledge of predictive analytics techniques, while continuing to learn how to apply emerging AI techniques and architectures to solve evolving business needs.

Responsibilities:

• Applies knowledge of sophisticated analytics techniques to manipulate large structured and unstructured data sets in order to generate insights to inform business decisions.
• Buildout in-house AI/ML products with other team members, including data scientists, IT developers, and business stakeholders
• Develop NLP and other AI/ML models including LLMs, VLMs and GenAI applications to improve risk selection and pricing.
• Identifies and tests hypotheses, ensuring statistical significance, as part of building and developing 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 trade-offs between and among choices, with a reasonable view into likely outcomes.
• Customizes analytic solutions to specific client needs.
• Responsible for smaller components of projects of moderate complexity.
• Regularly engages with the data science community and participates in cross-functional working groups.

Qualifications

• Solid knowledge of predictive analytics techniques and statistical diagnostics of models.
• Advanced fluency in Python programming including model deployment. Cloud deployment experience will be a plus.
• Advanced knowledge of SQL and data wrangling is required.
• Ability to demonstrate industry experience taking problems and translating them into analytics / data science solutions is a plus.
• Demonstrated ability to exchange ideas and convey complex information clearly and concisely.
• Has a value-driven perspective with regard to understanding of work context and impact.
• Candidate should possess a minimum of 2-3 yrs. of corporate experience(with a degree in math, computer science or statistics) or have a master’s degree in Computer science with some corporate experience in financial services