Data Scientist I (Dsap)

About the Business

LexisNexis Risk Solutions is the essential partner in the assessment of risk. Within Insurance, we provide customers with solutions and decision tools that combine public and industry specific content with advanced technology and analytics to assist them in evaluating and predicting risk and enhancing operational efficiency. Our insurance risk solutions help drive better data-driven decisions across the insurance policy lifecycle - all while reducing risk. You can learn more about LexisNexis Risk at the link below. https://risk.lexisnexis.com/insurance

About our Team:

The Insurance Analytics team are the trusted leaders in analytics excellence, delivering innovative, data-driven solutions through cutting-edge data science and strategic risk solutions to drive market leadership, impactful change, and lasting value for our customers and stakeholders. The team is responsible for new product innovation, model development, and creating actionable insights for our customers. We work closely with the Vertical and Product teams to design and implement new solutions for the insurance and OEM markets. By harnessing the power of data, our analytics team empowers insurers to make informed decisions, optimize risk segmentation, and enhance underwriting strategies, ultimately driving success in an ever-evolving insurance landscape.

About the Role

We are seeking a highly motivated data scientist to join the Data Science Accelerator Program (DSAP) that provides broad learning opportunities across the Data Science organization within the LexisNexis Insurance Division. Successful candidates will holistically learn our business during this two-year program while rotating through a series of Analytical product development related assignments and projects. The role will be Hybrid located at our headquarters in Alpharetta, GA and may include some travel.

Responsibilities:

• Assist with assembling, cleaning, and organizing large datasets to prepare them for analysis.
• Support team members in merging and parsing data to uncover trends and patterns.
• Learn to