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Charlotte, NC

Internship

Hybrid

Intern

The Hartford is an insurance company that aims to make a difference and is offering an 11-week Data Science Summer Internship Program designed to prepare interns for a career in Data Science. As a Data Scientist Intern, you will develop machine learning and artificial intelligence solutions while collaborating with teammates and networking across the Data Science & Analytics community.

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Responsibilities

Assist in creating statistical models, algorithms, and machine learning techniques to achieve financial objectives and solve business problems
Participate in reviewing work with business partners and team members on an ongoing basis to calibrate deliverables against expectations
Assist in identifying and assessing the value of new data sources and analytical techniques to ensure ongoing competitive advantage
Participate in the creation and deployment of long-term tools to continually evolve the business
Contribute to the successful implementation of strategies to achieve targeted business objectives
Develop knowledge of The Hartford's formal and informal structures, business processes, and data sources in your area of expertise
Remain current on research techniques and become familiar with state-of-the-art tools applicable to your function
Provide economic, qualitative, and statistical support to ensure accuracy of characteristics and metrics being applied to business decisions
Prepare and deliver presentations that translate data-driven insights, model results, and recommendations to varying business partners
Document work clearly to ensure effective knowledge sharing within the team

Qualification

Required

Must be authorized to work in the United States without sponsorship now, or in the future
Must be working towards a Master’s or Ph.D. in Statistics, Applied Mathematics, Quantitative Economics, Actuarial Science, Data Science, Computer Science, or a similar analytical field
Strong communication skills for explaining methodologies, visualizations, and recommendations to non-technical audiences and vice versa
Able to maintain organized project notes, version history, and technical documentation for team reference
Exposure to statistical modeling, inference, and building machine learning algorithms in an analytical programming language like Python or R

Preferred

Exposure to building modeling solutions in cloud-native environments, such as Sagemaker, a plus
Exposure to SQL and navigating databases to extract relevant attributes a plus
Exposure to Unix and Git a plus