An internship in Asset Management is more than a summer job. Building your resume, networking, and gaining valuable work experience are just a few benefits of participating in a paid internship at Fidelity Investments!
Looking for an outstanding opportunity to demonstrate your analytical attitude to impact financial decision-making? Data Analytics Internships within Fidelity’s Asset Management division give students the opportunity to do just that. These groups are seeking students with technical backgrounds who are interested in using those skills to assist our decision-making processes.
Education and Experience
- Class of 2025 or 2026 Master’s or PhD degree
- Demonstrated experience/interest/skill in data science analysis, data visualization or research
- Academic or intellectual interest in financial markets
- Excellent communication, presentation, and writing skills, as well as the ability to clearly articulate findings
- Field(s) of study must be in a technical area (STEM) and/or Finance/Economics with a strong programming background
Skills and Knowledge
- Excellent oral and written communication skills
- Strong analytical, quantitative, and problem-solving skills
- Must be able to code in Python and must have exposure to SQL
Intern Program Dates
June 2nd, 2025 through August 8th, 2025
Advanced Data Analytics Internship
The Data Analytics Intern will work closely within the Advanced Data Analytics Team to provide unique analysis supporting research analysts and portfolio managers.
Responsibilities include:
- Using mathematic, economic and data science techniques to collect, process, and analyze alternative data sets
- Developing knowledge and insights from large and/or unstructured data sets
- Web scraping, text analytics, and automated reporting
- Assisting with various projects to support stock-specific work
Interns may use resources including Wall Street contacts and internal groups like data and systems support, market data, and research services.
We are seeking students who are skilled in modern analytic techniques (e.g. Python, R, pandas, TensorFlow, scikit-learn, etc.)
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