Data Scientist - Actuarial

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.

For more information, visit www.libertymutualinsurance.com.

Description

Are you excited to help shape the future of AI at LII, tackling real business challenges that matter in your region? Join our energetic Regional Data Science Team and play a hands-on role in delivering innovative, AI-powered solutions that create measurable impact for our local business lines. As a Data Scientist, youll work alongside experienced data scientists, actuaries, engineers, and business leaders to accelerate experimentation and deliver high-value projects aligned with your regions priorities.

In this role, youll bring together your actuarial skills and passion for data science, using Python and advanced analytics to help solve critical challenges in pricing, risk assessment, reserving, and customer experience. Youll have opportunities to learn, grow, and see the direct results of your work in the insurance business, collaborating closely with the Center of Excellence team and benefiting from global best practices.

Responsibilities:

• Collaborate with regional data science, actuarial, and business teams to design and execute time-boxed experiments and analyses that address high-priority insurance business challenges.
• Support the delivery of key data science and actuarial solutionsapplying statistical and actuarial methods to real-world problems in pricing, risk selection, claims, or reserving.
• Build and validate predictive models and actuarial analyses using Python, leveraging libraries such as pandas, NumPy, scikit-learn, and actuarial modeling techniques.
• Stay flexible and responsivequickly adapting to evolving business needs and shifting focus to address immediate, high-value deliverables as directed by your Regional Data Science Manager.
• Help manage and prioritize your teams project backlog, balancing new opportunities with critical business timelines.
• Analyze results and develop insights that clearly demonstrate ROI and practical impact for your local business lines, presenting findings in ways that resonate with both technical and non-technical stakeholders.
• Contribute to the standardization and improvement of modeling, reporting, and analytical processes in partnership with both regional teams and the CoE.
• Work closely with actuaries, data scientists, engineers, IT, and business stakeholderslocally and globallyto share knowledge and leverage best practices.
• Stay proactive about emerging trends in AI, data science, actuarial science, and MLOps, bringing fresh ideas to the team.

Qualifications:

• Bachelors or Masters degree in Actuarial Science, Data Science, Mathematics, Statistics, Computer Science, Engineering, or a related field.
• Around 3 years of hands-on experience in data science and/or actuarial analysis, ideally within insurance or a related financial services context.
• Practical experience in Python for statistical modeling, data manipulation, and machine learning (e.g., pandas, NumPy, scikit-learn), and exposure to actuarial software or techniques (e.g., reserving methods, GLMs, stochastic modeling).
• Strong analytical and problem-solving skills, with the ability to translate insurance business questions into robust data science and actuarial solutions.
• Understanding of core actuarial concepts: loss modeling, rate-making, risk classification, reserving, and exposure analysis.
• Ability to communicate technical findings and insights effectively to actuarial, data science, and business audiences.
• Demonstrated flexibility and adaptabilityable to pivot quickly and work on the most pressing business deliverables as priorities evolve.
• Eagerness to learn, innovate, and contribute to a collaborative, high-energy team environment.
• Progress towards actuarial credentials (such as ASA, ACAS, or equivalent) is a plus.