the road to a summer industry internship

documenting all the tips and resources I've accumulated during my internship search

Below is a random conglomeration of tips, tricks, and resources I’ve come across during my search for an industry internship for Summer 2023. For reference, I am a PhD Candidate in linguistics with a background in computational psycholinguistics, and I am targeting positions in data science (not ML/NLP) or language analytics. Many of the resources below are a product of my participation in the UCD Leaders for the Future program, conducting informational interviews with linguists in industry, or podcasts/other blog posts shared by others (mostly on Twitter) that have provided valuable information.

Informational Interviews

To better understand the best way to approach securing my first internship in industry, I reached out to linguists in tech to request informational interviews. These individuals were either alumni from my program at UCD, a friend of a friend, or names I came across in podcasts/on Twitter who were regularly engaging in the space of career mentorship for non-academic linguists.

Meilin Zhan (Airbnb)

Meilin has a PhD in Linguistics from MIT and is currently a data scientist at Airbnb. She’s written about her experience interning at Airbnb in this blog post. Here are some of the key takeaways I learned from our chat:

• Meilin got her first industry internship (in 2018) the old fashioned way - at a career fair. She highly recommended attending these to learn about opportunities you might otherwise miss!
• Though she now works in a data science role, much of her programming experience was “learn by doing.” When asked what the best way was to demonstrate quantitative/technical skills when you don’t explicitly have a CS or Engineering degree, she suggested doing individual data science projects to display on GitHub or in some other online format. Crucially, she also said that hiring managers do not care about online certificates in data science, ML, etc. - the portfolio will get you the initial interview, and then they’ll evaluate your skills during a technical or coding interview.
• She noted that, as of Oct 2022, Airbnb was moving away from entry-level positions for data scientists and moving more towards software engineers. She mentioned that hiring for data scientists seems to have slowed as needs for the company have shifted.
• Her biggest piece of advice for the internship application process was to broaden your search: you may not find positions relevant to your domain-specific knowledge (in this case, positions specifically for language data scientists), but general experience in the long run is going to be better than no experience at all if the positions you’re looking for don’t exist or are few and far between.

Skills

Below is a list of technical skills commonly required for different types of internships in industry, provided by Meilin Zhan (mentioned above).

Key skills for all technical internships

1. statistics (resource: Practical Statistics for Data Scientists by Peter Bruce & Andrew Bruce)
2. probability (resource: classes on brilliant.org)
3. A/B testing (online controlled experiments in industry; resource: free A/B testing course by Google; Trustworthy Online Controlled Experiments by Kohavi, Tang, Xu)
4. communication and presentation

Key skills for Data Science internships

1. SQL (resource: example SQL tutorial)
2. R/Python ..a. data manipulation (e.g., dyplr in R, pandas in Python) ..b. data visualization (e.g., ggplot2 in R, matplotlib in Python) ..c. data analysis/modeling (e.g., logistic regression) ..d. general programming to implement a function (e.g., simulate throwing a die; generate random numbers from a distribution)
3. product sense (important for product analytics) (resource: Cracking the PM Interview by Gayle McDowell; Airbnb tech blogs; Facebook tech blogs)
4. machine learning basics (e.g., bias vs. variance; how to deal with overfitting; gradient descent)
5. basic data structure and algorithms knowledge

Key skills for Machine Learning Engineer internships

1. higher coding skills (for any position that specifically lists “engineer”)
2. ML algorithms (e.g., LeetCode; do 50 easy problems, 30-40 medium and hard problems to be competitive; follow this list of Leetcode patterns by topics)
3. knowledge of common and recent ML algorithms + ability to implement them in code
4. demonstration of some ML project experience is a plus

Key skills for Research internships

1. relevant research experience for specific team (e.g., NLP)
2. data structure & algorithm knowledge (e.g., tree traversal, binary tree search)
3. ML algorithms if relevant