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Job Description Summary
Job Description
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Nationwide Insurance is currently seeking candidates for multiple Behavioral Analytics Intern positions, based within the Enterprise Analytics Office, lasting from approximately mid-May through mid-August of 2022 (based on schedules). Within our team, you will apply your quantitative research background to tough business problems in a highly collaborative, diverse team. You will work alongside full-time Behavioral Analytics associates and present findings to business stakeholders.
Our team is dedicated to working with business partners to develop integrated, end-to-end solutions that contribute to the company’s bottom line. You’ll have the opportunity to work with real-world data to predict, explain and influence the behaviors of Nationwide’s members and partners. You will use one or more modeling techniques, such as frequentist/Bayesian statistical models and supervised/unsupervised machine learning algorithms using enterprise-class analytic software and platforms, such as Python, R, Tableau, Teradata and Amazon Web Services.
Candidates pursuing a degree in quantitative Social Sciences, Statistics, or related fields will be considered at the undergraduate or graduate levels. Experience with quantitative research, study design and experimental design is preferred. Special consideration will be given to candidates with graduation dates in 2022.
Job Summary
The Behavioral Analytics Intern will take an active role on one or more projects within Nationwide’s Enterprise Analytics Office and participate in many or all phases of the analytic lifecycle, including:
- Integrating relevant internal and external research findings to generate hypotheses and help business partners make informed decisions.
- Building predictive and explanatory models to generate actionable insights and practical solutions to business problems.
- Crafting deliverables that provide clear and concise summaries of findings and recommendations using impactful storylines and data visualization geared to both technical and non-technical audiences.
- Upholding a high standard for methodological rigor, sound statistical inference, and robust model validation.