Scott Sieger

Analyst with 4+ years of experience applying large-scale analytics and statistical models to diverse business problems.

Experience spans statistical model development, validation, performance evaluation, and model risk applications across pricing, and risk applications. Proven track record partnering with data science, engineering, and business stakeholders to deploy data-driven analytics solutions at enterprise scale.

Advanced Analytics Analyst at Liberty Mutual (2025-06 – Present)

• Lead statistical validation of catastrophe risk models covering billions in insured exposure, using goodness-of-fit testing to confirm models are accurate and reliable.
• Partner with Product, Underwriting, and Engineering teams to translate model outputs into pricing and growth recommendations, and to communicate to technical and non-technical stakeholders.

Senior Associate, Risk Analytics at Liberty Mutual (2023-06 – 2025-06)

• Cleaned and prepared data using Python, SAS, and Snowflake to support ongoing catastrophe risk model validation and monitoring.
• Automated data manipulation and validation workflows, reducing quarterly processing time by over one week while improving reproducibility.

Analyst Development Program at Liberty Mutual (2022-06 – 2023-06)

• Developed and validated XGBoost classification model to predict Good Student Discount eligibility for auto policies, improving targeting accuracy and reducing manual review. Evaluated performance of model using AUC – ROC methodologies.
• Applied PCA and K-Means clustering to build a territory segmentation framework using HLDI auto data to support pricing and geographic risk analysis.
• Built a Tableau dashboard to communicate pricing factors to underwriters for a worker's compensation pricing model to help with model buy-in from stakeholders.
• Translated auto pricing elastic net model from SAS to Python enabling version control, automated testing, and more consistent model performance tracking.

Data Science Intern at Liberty Mutual (2021-05 – 2021-08)

• Designed a predictive claims segmentation framework using SQL, Python, and Power BI to support claims analysis on the Model Monitoring and Enhancement team.

B.A. in Economics – Boston College (2022)

Certificate in Applied Data Science & AI – MIT Professional Education (2026)