FAQs
What is the job title for this position?
The job title is Student Opportunities - Data Science Intern (Graduate-level) - Summer 2025 (Hybrid).
Where is the internship located?
The internship is based at the corporate headquarters in Fairfield, Ohio.
What are the main duties of the intern?
The main duties include examining raw data for machine learning model training, fine-tuning deep learning models, running analyses to distill business insights, and utilizing analytical skills to identify solutions for business challenges.
What are the preferred qualifications for candidates?
Candidates should be enrolled in a graduate-level program (preferably a PhD candidate) in a quantitative field, have relevant coursework in computer vision and deep learning, and be proficient in Python and Microsoft Office tools.
Is there a GPA requirement for this position?
Yes, candidates are preferred to have a cumulative GPA of 3.0 or higher and must be in good academic standing.
What is the starting pay for this internship?
The starting pay begins at $26 per hour, based on the applicant’s education, experience, knowledge, skills, and abilities.
How many hours per week is the intern expected to work?
The intern must be able to work up to 40 hours per week.
What skills are important for success in this internship?
Important skills include patience, attention to detail, knowledge in computer vision and deep learning, strong problem-solving abilities, curiosity about data, and good collaboration and communication skills.
Are unofficial transcripts acceptable for the application process?
Yes, unofficial transcripts are accepted with the application.
Will there be opportunities for professional development during this internship?
Yes, the company promotes a lifelong learning approach and provides the tools and training necessary for success in the insurance industry.
Is diversity valued in your workforce?
Yes, the organization values a diverse workforce and is committed to providing equal employment opportunities to all qualified persons without regard to various protected characteristics.