LexisNexis Risk Solutions aims to provide insurance customers with solutions and decision tools that leverage advanced technology and analytics to help them evaluate and predict risk, thereby enhancing operational efficiency and driving better data-driven decisions across the insurance policy lifecycle.
Requirements
- Coursework or project experience in Python or R; familiarity with SQL is a plus
- Interest in data analysis, predictive modeling, and machine learning.
- Exposure to data visualization tools (e.g., Tableau, Power BI, Matplotlib, Seaborn) is desirable.
- Strong problem-solving skills with a willingness to learn and adapt.
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 apply statistical and machine learning techniques under the guidance of senior analysts.
- Help document model code, track results, and contribute to validating predictive models.
- Assist in creating attributes and rules from raw data to support analytic projects.
- Contribute to data visualization efforts to communicate findings.
- Participate in preparing responses to client inquiries regarding data, score calculations, or analytic outputs.
Other
- Hold a bachelor’s in data science, Math, Stats, Comp Sci, or other quantitative/applied math field.
- Ability to communicate ideas clearly and work collaboratively with others.
- The role will be Hybrid located at our headquarters in Alpharetta, GA and may include some travel.
- Document work clearly and practice presenting results in a team setting.
