Data Scientist

Kemper•Boston, MA

7d•$93,300 - $155,200•Hybrid

About The Position

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 building and developing analytical solutions, especially in updating existing solutions.

Requirements

Graduate degree in Mathematics, Statistics, Engineering, or other STEM field with 2-4 years of experience working in a data science/analytics environment.
Strong proficiency in Python, including experience with common data science libraries such as pandas, NumPy, scikit-learn, SciPy, and visualization libraries.
Proficiency writing and interpreting SQL queries for data extraction and analysis
Solid understanding of statistical modeling and machine learning concepts, including model training, validation, performance evaluation, and interpretation.
Hands-on experience with common statistical and machine learning techniques, such as generalized linear models, regularized regression, tree-based models, ensemble methods, clustering, or neural networks.
Ability to write readable, maintainable, and well-documented Python code.
Experience working with structured datasets from relational databases, delimited files, data frames, and other common data formats.
Excellent overall communication skills, particularly possessing the ability to translate technical results for wide audiences.
Ability to work independently on defined assignments while seeking guidance appropriately on complex or ambiguous problems.

Nice To Haves

PhD in Mathematics, Statistics, Engineering, or other STEM field with related industry experience preferred.
Experience with time series modeling techniques, such as ARIMA, forecasting, or trend analysis
Prior experience in insurance, financial services, pricing, risk modeling, or a related analytical business environment is preferred but not required.
Experience with Git, GitLab, or other version control and collaborative development tools.
Exposure to cloud platforms such as AWS, Azure, Databricks, or similar environments.
Exposure to MLOps concepts such as model packaging, version control, CI/CD workflows, reproducible pipelines, and model deployment.

Responsibilities

Independently designs and develops statistical models and analytical solutions.
Develops and automates analytical processes that can be deployed throughout the organization to solve recurring analytics needs.
Communicates project progress and challenges to the working team.
Develops database, technical, and business knowledge.
Documents modeling assumptions, methodology, code, and analytical results clearly and consistently.
Participates in model reviews, code reviews, and technical discussions with data science peers.