Liberty Mutual's US Retail Markets business needs to predict future risk and customer needs, driving smarter decisions, faster and more efficient processes, and better outcomes through data and analytics.
Requirements
- 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).
- 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.
Responsibilities
- 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.
- 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.
- 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.
Other
- 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 care about why the work matters with a demonstrated focus on real-world impact and outcomes, not just technical model performance.
- 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.
