Senior Data Scientist

Great American Insurance Company•Cincinnati, OH

3d•Hybrid

About The Position

Great American Risk Solutions (GARS), a growing wholesale excess and surplus insurance business focused on complex and specialized commercial risks, is seeking a Senior Data Scientist for its Analytics Team. The role provides technical leadership and strategic partnership to advance analytics delivery, modernize tools, and scale capabilities across the business. Employees are expected to work on-site four days per week, with one remote day. Core in-office days are Tuesday through Thursday; the fourth on-site day is determined by business needs. The Senior Data Scientist shapes data science strategy and leads predictive modeling and analytics work across underwriting, pricing, claims, and customer retention. The role takes models from study design through production, analyzes complex structured and unstructured data, and monitors model performance after deployment. It also develops data pipelines, automated reporting, dashboards, and solutions using artificial intelligence, machine learning, and automation. Working with actuarial, underwriting, claims, product, data engineering, and business leadership teams, the role translates business needs into technical specifications and communicates insights and recommendations to senior leaders. It ensures regulatory and internal governance requirements are met, including documentation, explainability, fairness testing, and validation. The role also mentors junior team members, supports team standards and training, and promotes analytics as a business competency. A master’s degree in a relevant quantitative field or equivalent work experience is required. A PhD is preferred, as is experience with geospatial data, telematics, or catastrophe modeling. The position requires at least six years of relevant experience. This position is not eligible for employment visa sponsorship; applicants must be authorized to work in the United States without current or future sponsorship.

Requirements

Expert proficiency in Python and core machine learning libraries such as scikit-learn, XGBoost, and LightGBM.
Advanced proficiency creating, retrieving, and optimizing data with SQL in Snowflake or a comparable SQL database.
Experience with a major cloud platform: AWS, Azure, or GCP.
Experience with data pipeline tools such as Airflow or dbt.
Strong proficiency in data visualization and dashboard development using Tableau or Power BI.
Ability to present complex problems and solutions to senior leaders.
Master’s degree in data science, statistics, computer science, actuarial science, or a related quantitative field, or equivalent work experience.
Generally, at least 6 years of experience exploring and manipulating large datasets, using structured and unstructured data, applying predictive modeling, and taking models from concept to production.
Proven experience in model optimization techniques, machine learning, and analytical programming.
Authorization to work in the United States without the need for current or future employment visa sponsorship.

Nice To Haves

Experience with R.
Experience with geospatial data, telematics, or catastrophe modeling.
PhD in a relevant field.

Responsibilities

Shape data science strategy and apply advanced subject matter expertise to identify business potential.
Lead small to moderately complex projects from design through deployment, working independently with limited supervision.
Design, develop, validate, and deploy predictive models for underwriting, pricing, claims, and customer retention, from initial study design through production rollout.
Analyze large, complex, high-dimensional structured and unstructured datasets to identify trends, patterns, risk factors, and business opportunities.
Create models, simulations, and optimization routines, selecting techniques appropriate to the business problem.
Monitor and maintain production model performance, including drift detection, retraining, and ongoing tracking and follow-up studies.
Design and implement data pipelines, automated reporting tools, and dashboards that track business metrics and provide actionable insights.
Partner with data engineers on data quality, accessibility, and scalability, and define strategies for acquiring and sharing data and analytics.
Identify and lead delivery of artificial intelligence, machine learning, and automation solutions that replace manual work for revenue-generating teams.
Partner with actuarial, underwriting, claims, and product teams to translate business problems into technical requirements and data science specifications.
Advise business leaders and use data and analytics to inform decisions, considering business impact alongside model metrics.
Communicate insights and recommendations to senior leadership through data storytelling and visualization.
Provide input to GARS product roadmaps and participate in agile planning and documentation for minimum viable products and long-term roadmaps.
Ensure models meet insurance regulatory requirements and internal governance standards, including documentation, explainability, bias and fairness testing, and validation.
Evaluate new data sources, modeling techniques, and technologies, and bring proven approaches into production.
Drive accountability for recognizing the value and business results of data science work.
Mentor junior data scientists and analysts through technical guidance, code and model reviews, and support for team standards in modeling, coding, and documentation.
Help build a culture that values analytics, including leading training sessions and advocating for analytics across GARS.
Perform other duties as assigned.