Bank of America Internships: Quantitative Data Analytics Summer Analyst 2027

Bank of America internships for 2027 include the Quantitative Data Analytics Summer Analyst Program, a 10-week internship for students who want to apply mathematics, statistics, programming and data analysis to financial problems. The program is hiring in Atlanta, Charlotte, Chicago and the New York City metro area, and applications are scheduled to close November 7, 2026. Candidates should be pursuing a bachelor’s degree or a bachelor’s-direct-to-master’s program with a graduation date between November 2027 and August 2028. Preferred areas of study include mathematics, statistics, economics, data science, computer science and financial engineering.

This Program Has Two Different Analytical Profiles

Applicants can be considered for Quantitative Analyst and Data Analyst profiles. The distinction matters because the skills overlap but the daily work can be different.

Quantitative Analysts use mathematical and statistical models to solve financial and risk-management problems. Students interested in this track should be comfortable with modeling, probability, statistics and programming languages such as Python, C++, R, MATLAB or Java.

Data Analysts focus more heavily on collecting, cleaning, integrating and interpreting data. Visualization and data-tool experience can be particularly useful, including Tableau, Hadoop, Power BI, Python, R, SQL or Java.

Before applying, decide which profile better matches your strongest evidence. You will have the opportunity to rank profile preferences, so your resume should support the ranking you choose.

The Program Spans Five Lines of Business

Interns can be aligned with Global Risk Management, the Chief Financial Officer Group, Enterprise Credit, Consumer & Small Business Banking or Analytics, Modeling & Insights.

These groups use data differently. Global Risk Management may focus on risk measurement and governance. The CFO Group uses analytics around finance, capital, treasury, reporting and planning. Enterprise Credit uses data for underwriting, monitoring and credit systems. Consumer & Small Business Banking uses analytics to understand products, customers and growth. Analytics, Modeling & Insights focuses on client behavior, measurement and decision science.

Students do not need to be experts in all five areas. They should understand that the business context affects the questions being asked even when the technical tools are similar.

Data Cleaning Is Not a Minor Task

A large part of useful analytics happens before modeling. Data may be incomplete, duplicated, inconsistent or stored in several systems.

Students should be able to explain how they check data quality. What happens when values are missing? How do you identify duplicates? How do you decide whether two fields represent the same concept?

A project involving messy public data can be stronger than a polished classroom dataset if you can explain the work required to make the information usable.

Quantitative Modeling Needs a Financial Question

A model is useful only when it answers a question the business cares about. For a bank, that could involve credit risk, liquidity, client behavior, forecasting or another financial challenge.

Students should practise explaining models in terms of inputs, assumptions, outputs and limitations. If you built a regression, what decision was it supporting? If you trained a machine-learning model, how did you evaluate whether it generalized?

A strong analyst can also explain when a complicated model is unnecessary. Sometimes a simpler method is easier to interpret and performs well enough for the business need.

Programming Skills Are Expected at an Advanced Student Level

The program expects strong programming and analytical-tool skills. Python, SQL, R, C++, MATLAB and Java are among the languages referenced.

Applicants should not copy every language into their resume. Use the ones you can demonstrate. If Python is your strongest language, highlight a substantial project and be ready to explain libraries, testing and data-handling decisions.

For SQL, practise joins, grouping, window functions and data validation. For R or MATLAB, show how the tool supported statistical or numerical analysis.

Visualization Is More Than Making a Chart Look Good

Data Analyst candidates can benefit from Tableau, Power BI or related visualization experience. A good visualization helps the reader understand a decision, not simply admire a dashboard.

When building a portfolio, explain why each metric is present. Avoid adding charts that do not change the interpretation. Make labels clear and ensure that scales do not exaggerate differences.

In an interview, be prepared to explain what action someone could take after seeing your dashboard.

Research and Public Data Can Be Part of the Work

Summer Analysts may conduct research using both internal and public information to understand risk or financial management at the business-line and enterprise level.

This means students should be comfortable finding information, evaluating quality and combining different sources carefully. A research project can demonstrate this even if it was not in banking.

Explain how you determined whether data was trustworthy and how you handled conflicting information.

The Academic Window Is Specific

Eligible candidates need a final graduation date between November 2027 and August 2028. A minimum GPA of 3.5 is preferred.

Put your expected graduation date and GPA clearly in the education section so the recruiter can verify fit quickly. Students outside the graduation window should look at other Bank of America programs rather than assuming the requirement is flexible.

Location Preference Is Part of the Application

The role has headcount in Atlanta, Charlotte, Chicago and the New York City metro area. Applicants are asked to rank the locations where they would accept an offer.

Think about that ranking honestly. Do not rank a city highly if you know you cannot spend the summer there. Consider housing, transportation and any university commitments.

Assignments depend on business need and skills, so neither the exact line of business nor role profile is guaranteed.

The Internship Runs 40 Hours Per Week

This is a full-time, 10-week summer program with 40 hours per week. It begins with formal training and continues with on-the-desk work, mentorship, professional development, networking, volunteer activities and speaker sessions.

The program is designed to give interns work similar to what full-time analysts do at an early-career level. That means accuracy and professional communication matter from the start.

How to Prepare for Technical Interviews

Review probability, statistics, data structures and the programming tools listed on your resume. Quantitative candidates should practise modeling questions and explaining assumptions. Data candidates should practise SQL, data cleaning and visualization.

Every candidate should be prepared to discuss one project end to end. Explain the question, data, method, result and limitations. If you cannot explain why a model or visualization was chosen, the project may not provide strong interview evidence.

Apply Before the Deadline, Not On It

The application deadline is November 7, 2026, but recruiting and assessments occur on a rolling basis. Students can therefore improve their chances by applying when their materials are ready.

Waiting until the final day offers little advantage, especially if interviews begin earlier. Make sure your profile ranking, location preferences and resume all tell a consistent story.

To apply for the Bank of America Quantitative Data Analytics Summer Analyst Program for 2027, use job ID 14420 and submit before November 7, 2026. Confirm that your graduation date falls between November 2027 and August 2028 and that your academic background and programming skills support either the Quantitative Analyst or Data Analyst profile. Rank your preferred business profile and locations thoughtfully, and prepare a resume that shows real work with data, modeling, programming or visualization rather than a list of tools without evidence.