[Remote] Analyst I, Data Science
Note: The job is a remote job and is open to candidates in USA. Liberty Mutual Insurance is seeking a Junior Data Scientist to join their Capacity Modeling and Optimization team within Claims and Service Data Science. The role involves building advanced forecasting and staffing optimization models, developing statistical models, and collaborating with partners to implement insights into practice.
Responsibilities
- Support development of scalable data pipelines and automated quality controls (schema, completeness, drift) across multiple sources
- Build statistical models for duration and action frequency; build exposure/phase level features and run exploratory/variance analyses
- Assist in clustering/segmentation and hypothesis testing to quantify efficiency and service impacts. Help build and run simulation models to compare assignment policies; analyze results and create scenario comparisons
- Help building work effort-based demand forecasts and staffing models; implement components of optimization models with supervision
- Maximize usable data by applying censoring aware methods, imputation, and reconciliation, document assumptions and ensure reproducibility
- Communicate findings through dashboards, reports, and presentations; collaborate with Claims and Service partners to move insights into practice
- Follow MLOps best practices (Git, reproducible workflows, experiment tracking) under mentorship
Skills
- Support development of scalable data pipelines and automated quality controls (schema, completeness, drift) across multiple sources
- Build statistical models for duration and action frequency; build exposure/phase level features and run exploratory/variance analyses
- Assist in clustering/segmentation and hypothesis testing to quantify efficiency and service impacts. Help build and run simulation models to compare assignment policies; analyze results and create scenario comparisons
- Help building work effort-based demand forecasts and staffing models; implement components of optimization models with supervision
- Maximize usable data by applying censoring aware methods, imputation, and reconciliation, document assumptions and ensure reproducibility
- Communicate findings through dashboards, reports, and presentations; collaborate with Claims and Service partners to move insights into practice
- Follow MLOps best practices (Git, reproducible workflows, experiment tracking) under mentorship
- Competencies typically acquired through a Master's degree (scientific field of study) and 0-1 years of relevant experience or a Bachelor's degree (scientific field of study) and 3+ years of relevant experience
- Solid foundation in statistics and ML: regression/GLM, inference, experimental design; familiarity with survival/censoring, time series, and hierarchical models
- Exposure to operations research and simulation: queueing concepts, discrete event or agent-based simulation; familiarity with OR Tools or Pyomo and SimPy is a plus
- Proficiency in Python and SQL; experience with pandas, NumPy, scikit learn, stats models; visualization using Plotly/Seaborn and dashboarding (e.g., Dash) is a plus
- Experience writing clean, tested code with version control (Git); familiarity with MLflow and workflow orchestration (e.g., Airflow) is a plus
- Comfort working with large, complex operational datasets; strong problem solving, communication, and collaboration skills
- Coursework or experience in claims/service operations or workforce management
- Familiarity with cloud platforms (AWS/GCP/Azure) and distributed processing (Spark)
- Exposure to Docker and CI/CD; experience deploying models or dashboards with supervision
Benefits
- Flexible Time Off Annual Accrual - days: 20
- Comprehensive benefits
- Workplace flexibility
- Professional development opportunities
- Opportunities provided through our Employee Resource Groups
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