Actuarial Data Scientist - Retention Modeling

Overview

This individual contributor position is responsible for supporting analytical projects on identifying business opportunities, answering business questions and developing business solutions. The team member will need to be able to understand business needs and create research and implementation plans using data, statistical models and machine learning techniques. The team member needs to have strong data and programming skills and be able to work closely with cross-functional teams of software engineers, data analysts and product leaders on business initiatives.

Geo-Salary Information

An in-person interview may be required during the hiring process

State specific pay scales for this role are as follows:

$94,458 to $179,048 (CA, NJ, NY, WA, HI, AK, MD, CT, RI, MA)

$85,871 to $162,771 (NV, OR, AZ, CO, WY, TX, ND, MN, MO, IL, WI, FL, GA, MI, OH, VA, PA, DE, VT, NH, ME)

$77,283 to $146,494 (UT, ID, MT, NM, SD, NE, KS, OK, IA, AR, LA, MS, AL, TN, KY, IN, SC, NC, WV)

The expected base salary for this position will vary depending on a number of factors, including relevant experience, skills and location.

Responsibilities

Essential Job Functions:

• The team member will work with various business departments to include UW, Claims, Marketing, and Advertising with understanding of business processes and business needs in these areas.
• Identify the right business sources related to the issues and opportunities, conduct project work as assigned and performs ad-hoc analyses using statistical and machine learning algorithms to help improve business processes and decision-making.
• Works independently with guidance and direction from advanced level data scientists and management.
• Participates in discussions with colleagues regarding analysis needs. Reviews and discusses analyses with colleagues and managers as necessary.
• Provide data support on projects and work closely with business on implementation through either analytical process or IT process.
• Create monitoring procedures and reports for projects implemented, and identify potential opportunities to improve accuracy and efficiency.
• Understand data sources from different applications and data infrastructure. Retrieves large amount of structured or unstructured data from various sources and apply advanced data manipulation techniques to prepare data for analysis.
• Reviews data for accuracy and optimizes methods of data retrieval when necessary. Create, maintain and improve dynamic reports, graphs and charts through dashboard and other and programming and MS Excel.
• Create procedures to streamline analysis procedures.
• Establish automated procedures to help reduce cost and improve efficiency.
• Answer questions and conduct ad hoc analysis from business on related reports.
• Communicate