“I Know What You Did Last Summer”: A guide to applying for data science internships
NYU Data Science upperclassmen discuss their experiences applying for internships.
If you’re agonizing over your resume, stressing about interviews, and feeling anxious about where you’re going to end up this summer — worry no more!
In the first week of September, the NYU Center for Data Science hosted a panel discussion titled “I Know What You Did Last Summer.” The panel featured three current undergraduate Data Science students at NYU who spoke in depth about their experiences applying for, securing, and working in an internship.
In this article, I’ll break down the highlights and important takeaways from the event, so you can tackle your own internship search with confidence!
Meet the Speakers
Mindy Wu (she/her) is an NYU senior, majoring in Computer Science & Data Science. During the 2022 Summer, Mindy was at KPMG — a Big Four accounting firm — as a Data Analytics Intern.
Keerthana Manivasakan (she/her) is an NYU senior, majoring in Computer Science and minoring in Data Science. Keerthana spent her summer at J.P. Morgan & Chase as a Software Engineering Intern.
Mitch Phillips (he/him) is a part-time student at NYU this semester, majoring in Data Science with a minor in Math. Mitch is currently interning at the Universal Display Corporation, in the quality assurance department.
The Prep
Before you even apply to openings, it is important to ensure that your materials are of the highest-quality, and fully representative of your skills and interests.
The three speakers emphasized the importance of fixing up your resume, portfolio, and technical skills in anticipation of the application process. Let’s go over each of these aspects in detail.
Resume
“It’s very important to write your resume really well. It gets your foot into the door.” — Mindy
Nowadays, most applications go through automated screenings before an actual human sees it. Consequently, it is important to create a resume that scores well when fed through a screener. NYU provides access to one such online resume review tool called VMock. The program makes use of natural language processing to give you specific and personalized feedback. You can upload your resume and receive suggestions up to 10 times for free.
Keerthana mentioned another resource for resume refinement: the Google Careers’ tips and advice page. In particular, Keerthana has found success using their X-Y-Z formula while writing her resume — Accomplished X as measured by Y doing Z.
For example: Increased tail wags of Dooglers by 75% over two days by placing treats outside of conference rooms. — Google Careers, exemplifying the X-Y-Z formula for resume writing.
Portfolio
“Have completed projects to your name .” — Mitch
Employers are looking to see that you not only have learned certain technical skills, but that you are able to apply the skills. Portfolio projects are critical for this purpose.
You can start off by polishing class projects and homework assignments to present to recruiters. When you find time, come up with your own project ideas that make use of tools you learned in your courses. Be creative and work on something that genuinely interests you! Data is found everywhere, so find data that relates to your hobbies or passions to work with. Ask for support from your peers and professors if you ever get stuck in the process.
Mitch suggests that you upload these projects to GitHub consistently. Companies are often looking at the languages, programs, and processes that you have experience with. Make sure you know the ins and outs of whatever you upload to GitHub. Keerthana reminded everyone that interviewers will inquire about your work, so if you feel unsure about anything, it’s best to leave it off your portfolio.
Technical Skills
An audience member asked the panelists, “How do you balance technical interview prep with school work?”
This is a concern for many students, who are already overwhelmed with assignments and work from their university courses. Having to do hours of LeetCode on top of that is unfeasible.
One possible path forward is to schedule weekly times to practice LeetCode or similar training. Mindy advises that you think of it as another class and routinely practice. Frequent, if not abundant, training can help slowly — but steadily — build your skills.
Mitch asks students to consider what would be the most valuable use of their limited time. Rather than focusing on brute programming, it might be more helpful to do mock-interviews and practice presenting your coding process.
Mindy said it’s important to prioritize pseudo coding, as even if you’re not able to execute your code successfully in a timed interview, the interviewer can see that you were on the right track with your pseudo code.
The Search
Finding Opportunities
“I filled out 120 applications, heard back from 10, got offers for 2.” — Mindy
To some extent, applying for internships is a numbers game. The field is incredibly competitive, and increasing the number of applications you complete can increase your chances of success.
Still, how does one find those suitable opportunities to apply to? The panelists reflected on their own journeys, and advised several different strategies.
Mitch described LinkedIn as his best friend — many recruiters use the social networking site, and even though it might seem like random people are messaging you — reply! It takes less than fifteen seconds and it might lead you down an unexpected, but rewarding, path.
Keerthana recommended several different resources for finding open positions. There are Github repositories (such as this one maintained by Pitt CSC) that keep track of tech opportunities at major companies. With targeted search terms and filtering, Google Jobs can also be very useful to find open positions. Be cautious, though! Keerthana warned that there can be some sketchy companies, so don’t give out your information blindly.
Both Keerthana and Mindy emphasized the importance of interacting with the people in your communities to find opportunities. Talking to upperclassmen, your professors, friends, and other people you know that have industry experience can aid your search. They might even be able to give you referrals into companies that you’re interested in, so it’s important to reach out.
Additionally, the NYU Center for Data Science has many career resources for students who have declared a data science major or minor. You can email cds-undergraduate@nyu.edu for more information.
Timeline
The panelists discussed that late-summer and early-fall is the best time to begin applying for summer internships. Companies are also actively recruiting on campus, so there are lots of opportunities to learn more about open positions and show your interest.After you submit applications, you’ll begin receiving requests for coding screeners and interviews.
The Interviews
Interviewing is often the most time and effort intensive portion of the application process. Each company’s process will differ slightly but there are several elements that are consistent:
- Coding screener
Coding screeners are often in the form of HackerRank or HireVue links. These timed activities ask you to solve a coding problem in the computer language of your choice. These are used to check coding proficiency at the standard the company needs.
- Recruiter call
After checking coding proficiency, or an initial review of your application materials, a recruiter may reach out requesting a short phone call. They will usually ask general questions about your experience and interest in the company.
- Behavioral interview
This interview is the more in-depth version of the recruiter call, and it often takes place with a member of the team that is hiring an intern. Questions asked are along the lines of, “What is a project you enjoyed working on and why?” or “Tell me about a time when you had to explain a technical concept to someone unfamiliar with it.” This is an opportunity to highlight your accomplishments, present your ability to work in a team, and showcase your interest in that specific company.
- Technical interview
The technical interview is similar to a coding screener, except this time there is an interviewer presenting the problem to you. The idea is for you to walk them through your thought process and explain your decisions as you attempt to find a solution.
The panelists had a plethora of great tips on how to navigate the interviewing process successfully.
Have confidence! The company needs you just as much as you need them, that’s why they are interviewing you. To avoid nerves, Keerthana encourages people to think about interviews as a conversation. If you know who your interviewer is beforehand, search them up on LinkedIn or Github and learn about their expertise. You may end up working with this person one day, so Mitch suggests being personable and engaged.
When talking about your own expertise, an optimal way to present your projects is with the STAR method. Here’s the breakdown:
- Situation: Start by setting the scene and establishing context.
- Task: What was your specific responsibility, or ‘task’ in the story?
- Action: Describe what decisions you made and what steps you took to achieve your goal.
- Result: Emphasize the results of your actions — be specific and numeric wherever possible.
Using this method will ensure that you don’t ramble on or run out of things to say too quickly.
It’s also important not to hesitate to ask clarifying questions. Make sure you understand what the interviewers are looking for in an answer. Keerthana suggests repeating the question back to the interviewer to ensure understanding.
And lastly — the interviewers will often end by asking you have if you have any questions for them. You should always have some! This is the best way to show interest in the team. Any question is better than nothing. The panelists offered some example questions:
- What projects is your team currently working on? Have there been significant challenges or successes?
- What are you looking for in an intern?
- Is there anything else I can tell you about myself?
- How do you see me fitting in your team?
- What kind of tech stack do you use?
- Consider asking some more technical questions: What types of data visualizations do you work with? What machine learning packages (scikit-learn, TensorFlow, Keras) does the team use?
The internship search is difficult and draining. Often, students feel unqualified and discouraged when facing the daunting steps. The panelists ended the session by discussing how to deal with such pervasive feelings.
Mitch reminded everyone that it’s okay to not know everything — just remain eager to learn. Putting in the time and effort is what’s most valuable. Even if you don’t immediately see progress, you are growing every time you complete a project, practice interviewing, and develop your technical skills.
