Location(s)
Alpharetta, Georgia, Bloomington, Illinois, Chicago, Illinois, Dallas, Texas, Jacksonville, Florida, San Antonio, Texas
Position Summary: Data Science is a driver of significant competitive advantage for Kemper and is critical to the organization's success. As a member of the Kemper Auto Data Science team, this position is responsible for independently designing, developing, implementing, and monitoring predictive modeling and analytical solutions that support pricing segmentation, product development, and profitable growth.
Position Responsibilities:- Independently designs, develops, validates, and implements statistical and machine-learning solutions for complex business problems.
- Owns significant analytical and modeling workstreams from problem definition through delivery and performance monitoring.
- Collaborates with data scientists, data engineers, and business partners to develop scalable analytical solutions.
- Develops reusable, well-documented analytical workflows using modern data science and cloud technologies.
- Manages priorities, deliverables, and timelines for assigned projects and communicates progress, risks, results, and recommendations to stakeholders.
- Participates in model and code reviews and recommends methodological or implementation enhancements.
- Provides technical guidance to less experienced team members and contributes to data science best practices.
Position Qualifications: Minimum Job Requirements- Bachelor's degree in Mathematics, Statistics, Engineering, or another STEM field with at least 8 years of relevant experience, or a graduate degree in a STEM field with at least 6 years of relevant experience in the insurance industry, data science/analytics, or a related environment. PhD in a STEM field preferred, with at least 4 years of relevant industry experience
- At least 4 years of firsthand experience with statistical modeling and AI/ML platforms
- Demonstrated experience independently developing and delivering statistical or machine-learning solutions
Required Job Skills- Strong proficiency in Python, including experience with common data science libraries such as pandas, NumPy, scikit-learn, SciPy, and visualization libraries.
- Strong proficiency in SQL for data extraction, transformation, validation, and analysis of large and complex datasets.
- Strong understanding of statistical modeling and machine learning concepts, including model design, feature development, training, validation, performance evaluation, interpretation, and monitoring.
- Hands-on experience with a range of statistical and machine learning techniques, such as generalized linear models, regularized regression, tree-based models, ensemble methods, clustering, or neural networks.
- Ability to develop readable, maintainable, modular, and well-documented Python code and reusable analytical workflows.
- Experience working with large and complex structured datasets from relational databases, delimited files, data frames, and other common data formats.
- Strong problem-solving skills with the ability to independently develop analytical approaches for complex or ambiguous business problems.
- Excellent communication skills, particularly the ability to translate technical methodologies, results, and recommendations for both technical and business audiences.
- Ability to independently manage significant analytical workstreams while collaborating effectively with data scientists, data engineers, and business partners.
- Experience participating in model reviews, code reviews, and technical discussions and providing constructive recommendations for improvement.
Preferred Qualifications- Prior experience in insurance, financial services, pricing, risk modeling, or a related analytical business environment.
- Experience applying predictive modeling techniques to pricing, risk, product, or other complex business applications.
- Experience with Git, GitLab, or other version control and collaborative development tools.
- Hands-on experience with cloud platforms such as AWS, Azure, Databricks, or similar environments for data science and machine learning workflows.
- Familiarity with MLOps practices such as model packaging, CI/CD workflows, reproducible pipelines, model deployment, and performance monitoring.
- Experience developing reusable or modular analytical frameworks that support scalable model development and implementation.
- Experience providing technical guidance or mentoring to less experienced data scientists.
Additional Information- This position can be worked in a hybrid arrangement from a local Kemper office. Remote options are available for non-local candidates.
- The range for this position is $104,300 to $173,300. When determining candidate offers, we consider experience, skills, education, certifications, and geographic location among other factors. This job is eligible for an annual discretionary bonus and Kemper benefits (Medical, Dental, Vision, PTO, 401k, etc.)
- Sponsorship is not accepted for this opportunity.
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