vhfdx.net

My Data Science Internship in New York: A Real Review

Written by

in

Quick outline:

  • Where I worked and how life felt
  • What I did day to day (with real tools and tasks)
  • Three real projects and what changed
  • What was rough
  • What worked well
  • Pay, hours, commute, food
  • Tips for you
  • Final verdict

So…was it worth it?

Short answer: yes. But it wasn’t smooth. My summer in New York was loud, fast, and full of data that didn’t always make sense. I learned a lot anyway. You know what? I even liked the chaos. (For an extended dive into all the gritty details, you can also skim my full data science internship review.)

Where I worked (and why it mattered)

I interned at a mid-size fintech near Flatiron. We were hybrid. I went in three days a week. The office had cold brew and very cold AC. I used a 13-inch laptop that felt tiny. I brought my own stand.

Pay was $42/hour (for context, Glassdoor lists the median data science intern salary at roughly the mid-$30s per hour). Full-time hours. No housing stipend. I shared a small room in Brooklyn with a friend. The Q train was my friend too. Most days I got off at 14th Street and walked past Madison Square Park. Sometimes I grabbed a chicken-and-rice plate from the halal cart. Great value. Messy keyboard.

The stack (real tools I touched)

  • Python (pandas, NumPy, scikit-learn, XGBoost)
  • SQL in Snowflake
  • Airflow for daily jobs
  • dbt for data models
  • Tableau for dashboards
  • GitHub + GitHub Actions
  • Jira tickets (some tidy, some vague)
  • Slack and Zoom (of course)

I lived inside Jupyter most days. I kept a scratch notebook and a “clean” one for my mentor. That saved me.

A normal day (more or less)

  • 10:00 standup on Zoom. Three lines: what I did, what I’ll do, what’s blocked.
  • Mornings: SQL pulls from Snowflake. Feature tables. Lots of joins.
  • Lunch outside if the sun wasn’t rude. A bagel if I needed a hug.
  • Afternoons: model tweaks, plots, and little tests. Push a PR before 5.
  • Code review. Fix naming. Add docstrings. Push again.

Some days were all meetings. Some days were quiet and very good. I learned to block two hours for deep work. Headphones helped.

Project 1: Churn model that stopped a small leak

Goal: flag users who might quit the paid plan in 30 days.

What I did:

  • Pulled 12 months of events and billing data.
  • Built features like days since last login, failed payment count, and time on help pages.
  • Trained XGBoost. Baseline AUC was 0.71. My model hit 0.79 on holdout.
  • Shipped scores to a Snowflake table each morning by Airflow.
  • Gave PMs a short guide on how to read the scores.

Real change:

  • The support team sent a friendly nudge to top-risk users.
  • Monthly churn dropped by 3.8 percentage points for that group over six weeks. It wasn’t magic. But it was cash saved.

Lesson:

  • Simple features beat fancy stuff when data is messy. Also, label drift is real. We set a retrain job every two weeks.

Project 2: A/B test on email subject lines

We tested two subject lines for a “yearly plan” promo.

What I did:

  • Helped design the split. 50/50. No weird overlaps.
  • Set a guardrail metric (unsub rate). Simple and smart.
  • Wrote a small script to run a t-test after 48 hours and 7 days.
  • Built a Tableau view for the marketing team.

Results:

  • Variant B had a +9.4% open rate and +2.1% click rate.
  • Unsubs were flat. That was key.
  • We shipped B for the next two weeks, then checked decay.

Lesson:

  • Pre-commit the stop rule. Or someone will peek and fuss.

Project 3: A dashboard people actually used

I built a “New User Health” dashboard in Tableau.

What I did:

  • Daily new users, 7-day stick rate, and a funnel from sign-up to first value.
  • One filter for device. One for region. That’s it. No clutter.
  • Added a small text box that said what “first value” means. Clear words help.

Impact:

  • Product leads used it every Monday. It drove two small UI tweaks. Stick rate went up 1.6 points the next month. Tiny, but real.

What was rough (and very real)

  • Dirty data: event names changed mid-year. No one told BI. I found it after a weird dip on a Friday. Hot panic. Cold fix.
  • Vague tickets: “Make model better.” Better how? I learned to ask, “What metric? By how much? By when?”
  • Laptop VPN: it broke once a week. I kept a local CSV so I could still write code.
  • Meetings across time zones: a 7 pm call with a PM in London. I brought tea.
  • If you like geeky metaphors, skim the propagation graphs on vhfdx.net; they’ll remind you how even noisy signals can carry valuable information.

What helped me not melt

  • I wrote a “daily notes” doc. Date, what I did, what I learned, and one blocker. My brain slept better.
  • I asked in Slack early. I added a tiny chart too. A picture beats a wall of text.
  • I used small PRs. Fast reviews. Less pain.
  • I kept a win list. Small wins count. It saved me on my mid-internship check-in.

Pay, hours, and the city stuff

  • $42/hour, 40 hours a week.
  • Commute: 35–45 minutes each way.
  • Lunch: $12–$15 if I stayed sane. Cheaper if I brought rice and eggs.
  • Weather: July was sticky. Office sweater needed. Subway was hot. Pack water.
  • Meetups: I went to PyData NYC and a Data Umbrella talk. I met two folks who later helped me with interview prep. Worth it.

For a broader benchmark, Salary.com’s New York estimate for a data scientist intern floats in the low-to-mid $40 range, so I felt fairly aligned.

Outside data meetups, the city’s social whirl can be just as intense as any Kaggle leaderboard. If you’re curious about exploring New York’s dating scene after a long day wrangling SQL, this honest rundown of the best Craigslist for sex apps highlights which modern platforms actually work, helping you avoid dead-end messages and get straight to making real connections. But maybe your projects will drag you out west for a conference or a client visit—say to Washington’s Tri-Cities—where the dating app landscape looks different; in that case, browsing the concise guide at Skip the Games Richland can clue you in on how to spot genuine local listings quickly and set up low-stress meetups without wasting precious post-work hours.

Real examples, kept short

  • A Snowflake query I wrote hit 1.2B rows. I added date and user_id filters and cut run time from 14 minutes to 90 seconds.
  • I replaced a Random Forest with XGBoost and tuned max_depth and learning_rate. Lift at top decile went from 2.1x to 3.0x.
  • I found a clock bug: events logged in UTC, but the dashboard read local time. A “drop at midnight” vanished after the fix.

Tips if you’re heading to a New York data science internship

  • Ask for one “ownable” project by week 2. Small is fine.
  • Keep a glossary of tables and columns. Share it. Be the person with the map.
  • Learn your team’s style guide. Naming saves time.
  • Bring a laptop stand and an HDMI adapter. Don’t count on the office.
  • Set alerts for model drift or job fails. Email, Slack, whatever works.
  • Go outside. Walk. Think. Ideas land when your eyes rest.

Thinking about the academic route before you intern? You might find my notes on how the UC Berkeley Data Science acceptance rate felt useful.

Who this fits (and who it doesn’t)

  • Good fit: you like messy puzzles, clear questions, and fast feedback.
  • Tough fit: you want clean data, quiet days, and long research time.

Still torn between leaning into dashboards or diving deep into models? My hands-on comparison of Business Intelligence vs Data Science might clarify which path matches your style.

Final verdict

I give it 4 out of

More posts