Location(s)

Alpharetta, Georgia, Chicago, Illinois, Jacksonville, Florida, St. Louis, Missouri

Details

Kemper is one of the nation’s leading specialized insurers. Our success is a direct reflection of the talented and diverse people who make a positive difference in the lives of our customers every day. We believe a high-performing culture, valuable opportunities for personal development and professional challenge, and a healthy work-life balance can be highly motivating and productive. Kemper’s products and services are making a real difference to our customers, who have unique and evolving needs. By joining our team, you are helping to provide an experience to our stakeholders that delivers on our promises.

Position Summary:

Data Science is a driver of significant competitive advantage for Kemper and is critical to the organization’s success. The Kemper Claims Data Science team has an immediate opening for an individual to make lasting impact through building and deploying predictive models.

Position Responsibilities:

• Work as part of a team to build predictive models designed to support the Kemper claims organization in areas such as fraud detection, vehicle damage estimation, adjuster triage, and quality management.
• Develop and automate predictive modeling processes that can be deployed through the organization to solve reoccurring analytics needs.
• Contribute to the improvement of internal modeling capabilities and data infrastructure by suggesting and applying tweaks to existing processes.
• Monitor deployed solutions and escalate as needed.
   

Position Qualifications:

• Proficient in Python programming language (scikit-learn, pandas, numpy, scipy, matplotlib, etc.).
• Proficiency and experience with several modeling techniques such as generalized linear models, decision trees, ensemble learning, regularized models (ridge/lasso/nets), clustering, and neural networks.
• Some experience with natural language processing is preferred.
• Prior experience working with imbalanced datasets and AWS is a plus.
• Ability to work with various data formats. This includes relational databases (especially), delimited text files, data frames, and JSONs.
• Excellent overall communication skills, particularly possessing the ability to translate technical results for wide audiences.
• Graduate degree in a quantitative field required: Mathematics, Statistics, CS, Physics, MIS, Economics, etc.
• 2+ years’ experience in a data science/ predictive analytics environment preferred.
• Self-directed and able to work with little supervision.
• Sponsorship is not accepted for this opportunity.
• This is a fully remote position that can be worked from any US-based home.

The range for this position is $81,400 to $138,900. 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.)  

Kemper is proud to be an equal opportunity employer. All applicants will be considered for employment without attention to race, color, religion, sex, sexual orientation, gender identity, national origin, veteran, disability status or any other status protected by the laws or regulations in the locations where we operate. We are committed to supporting diversity and equality across our organization and we work diligently to maintain a workplace free from discrimination. Kemper is focused on expanding our Diversity, Equity and Inclusion efforts to align with our vision, mission, and guiding principles.  Kemper does not accept unsolicited resumes through or from search firms or staffing agencies. All unsolicited resumes will be considered the property of Kemper and Kemper will not be obligated to pay a placement fee.

Kemper will never request personal information, such as your social security number or banking information, via text or email.  Additionally, Kemper does not use external messaging applications like WireApp or Skype to communicate with candidates.  If you receive such a message, delete it.  

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