Data Scientist

Description

The creative problem-solvers in Liberty Mutual's Insights & Solutions group harness the power of data, analytics and technology to develop innovative solutions that drive our US Retail Markets business forward and support a high-performing culture. This group brings together highly talented thinkers and doers ready to challenge the status quo and make an impact. As a member of this cross-functional group, you'll collaborate with teams across Liberty Mutual to deliver analysis that unlocks insights and sparks new, better ways of working.

The Insights & Solutions team provides critical analysis through five functional groups within US Retail Markets:
• Distribution, Marketing & Experience Insights & Solutions
• Data Office
• Claims & Service, Insights & Solutions
• Knowledge & Development Solutions
• Data Science

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.

• Participate in a six-month, hands-on program designed to accelerate your data science career with Liberty's cloud-native platforms and MLOps best practices.
• Build your understanding of business and insurance fundamentals through structured training, then join a project team aligned to your interests to tackle business challenges.
• Develop your skills by working with large datasets, building and validating predictive models, and turning prototypes into production-ready solutions.
• Translate quantitative findings into clear, actionable recommendations for technical and non-technical stakeholders.
• Grow your network and skills by teaming up with mentors and peers, joining community groups, and participating in lunch-and-learns, hackathons, and data science events.
• Transition into a permanent role on a US Data Science team that matches your interests and skills at the end of the program.

• You have a natural curiosity about how organizations use data and analytics to solve real problems and are comfortable operating in a business environment.
• You're skilled at breaking down complex ideas and communicating data-driven insights clearly to both technical and non-technical audiences.
• You have some hands-on experience with enterprise data science platforms (e.g. Snowflake, Databricks, Sagemaker) through coursework or projects and are eager to learn new technologies.
• You have a solid grasp of basic database concepts (e.g., tables, joins, simple queries).
• Familiar with exploratory data analysis and data wrangling techniques (e.g. handling missing values, simple transformations) to identify patterns and perform initial data cleaning.
• You're interested in code-first data science and reproducible results with Python from data gathering through model scoring using libraries like pandas and scikit-learn; familiar with version control (GitHub) and cloud platforms (AWS).
• You have strong foundational knowledge of statistics and simple machine learning algorithms (e.g. linear/logistic regression, decision trees) to analyze data, build models, and interpret results with standard metrics (e.g. accuracy, precision, recall).
• You care about why the work matters with a demonstrated focus on real-world impact and outcomes, not just technical model performance.
• Bachelor's or Master's degree (technical field of study) with relevant project or internship experience, and a demonstrated passion for data science.

• Data extraction and manipulation skills, EDA, transformations, and general linear models (GLM); preferred skills of basic CART and GLM.
• Foundational knowledge of predictive analytics tools.
• 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.
• Competencies typically acquired through a Master