Sr Predictive Modeling Scientist
Role details
Job location
Tech stack
Job description
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Predictive Model Development - Design, implement, and deploy statistical and machine-learning models (including time-series forecasting) to support underwriting, claims, pricing, and risk segmentation.
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Cross-Functional Collaboration - Partner with underwriting, claims, actuarial, and analytics teams to ensure models align with business objectives and regulatory requirements.
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Visualization & Communication - Create dashboards and visualizations that translate complex model outputs into actionable insights for decision-makers.
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Model Governance - Maintain documentation, version control, and compliance with regulatory and internal standards.
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Innovation & Research - Stay current with industry best practices, emerging modeling techniques, and new technologies.
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Data Pipeline & Automation - Build reproducible, scalable data pipelines and automate model monitoring, validation, and reporting processes., While performing general duties for this position, the employee is regularly required to sit, stand, and/or walk around (including the use of stairs). Other demands include the ability to openly communicate with others by talking, listening, comprehending, and reading; being able to lift light objects (<25 lbs); and using standard office equipment such as computers, printers, and phones. In addition, there is an occasional need to bend, twist, or squat down to open/close cabinets and reach for files or other standard office-type objects.
Requirements
Master's degree in Statistics, Data Science, Mathematics, Computer Science, Actuarial Science, or a related quantitative field,
Preferred Education:
PhD in a related quantitative field (Statistics, Data Science, Mathematics, Computer Science, Actuarial Science)
Experience:
- Demonstrated predictive modeling experience, including 4+ years of experience for candidates holding a Master's Demonstrated experience developing predictive models.
- Proficiency in Python or R, plus SQL.
- Strong communication skills with the ability to explain complex modeling concepts to underwriting, actuarial, and leadership stakeholders.
Preferred Experience
- At least 1 year of relevant predictive modeling experience acquired after completion of a PhD program.
- Knowledge of pricing models, GLMs, and regulatory considerations.
- Experience working in cloud environments (AWS, Azure, or GCP).
- Experience with MLOps tools (MLflow, SageMaker, Vertex AI).
- Background in GIS, geospatial modeling, or catastrophe modeling (wildfire, flood, wind).
- Experience with time-series forecasting for claims, exposure, or weather-driven perils.
- Experience building scalable data pipelines and automated model monitoring.
- Familiarity with insurance model governance and documentation standards.
- degree, or 1+ years of experience for candidates holding a PhD.
Benefits & conditions
Pulled from the full job description 401(k) Health insurance Paid time off Vision insurance Dental insurance Paid holidays, * Dynamic Environment: On-site role with a fast-paced and collaborative team culture. Results-driven office where your contributions make a real impact.
- Compensation: Competitive base pay and performance bonuses.
- Career Growth: Mentorship, growth tracks, and professional development.
- Benefits: Medical, dental, vision, 401k, paid holidays, PTO and more!
The office environment is fast-paced and collaborative. An employee must be willing and able to work their regularly assigned work schedule onsite, and in times of need, be able to work an extended schedule depending on company or departmental needs, project requirements, or customer demands.