Note: Role-play review based on real places, real tools, and real workflows.
Quick take? Chicago’s got range
I’ve worked data jobs that sat by the river, under the “L,” and yes, right by a deep dish spot. Some days felt electric. Some days felt like waiting on a stuck Airflow DAG. Both can be true. Let me explain. For anyone who wants the quick-hit version, I already logged a snapshot in this earlier review of trying data-science gigs across Chicago; consider the piece you’re reading the director’s cut.
How I got in the door
I used LinkedIn and Built In Chicago. I also met folks at PyData Chicago and ChiHackNight. That helped. A lot. I applied to 27 roles and heard back from 11. Not bad for a cold winter. If you’re weighing whether to go through headhunters instead, I put some stories on paper in my real take on data-science recruiters that breaks down the good, the bad, and the occasionally cringey.
Most teams asked for:
- A short SQL test (joins, windows, CTEs)
- A Python screen (Pandas and a bit of NumPy)
- A case or take-home (predict something or size an A/B test)
- A panel chat with a PM, an engineer, and a manager
Nothing wild. But time boxing helps. And please, label your notebooks.
Job 1: Food delivery, West Loop, big orange logo
This was Grubhub, right by Union Station. My team built delivery time models. We used Python, Spark on Databricks, and Airflow. The model that stuck was XGBoost. It used kitchen prep time, past orders, weather, and traffic. Simple stuff, but it moved the needle.
- Win: We cut mean error from about 9.5 minutes to 7.8. Fewer refund tickets. Happier drivers.
- Day to day: SQL in Snowflake, features in dbt, plots in Matplotlib and Seaborn. I shipped small updates weekly.
- Quirk: On-call got spicy. When a job failed at 2 a.m., I met my new best friend: CloudWatch logs.
- Culture: Friendly, fast, snack-heavy. Free lunch on Wednesdays made the sprint review feel like a party.
Pay then: I cleared around 135k base, plus a small bonus, plus stock that moved a bit.
Job 2: Health tech, River North, white lab coats on slides
Tempus pulled me in with the mission. Cancer data. Real lives. I worked on text data from notes. We used Python, spaCy, and a BERT model someone smarter than me set up on SageMaker. My part was cleaning, checking labels, and helping QA outputs.
- Win: We built a rules layer on top of model tags. That bumped precision a few points. Not huge, but safer for care teams.
- Day to day: HIPAA rules shaped our flow. Everything got logged. Everything had a review step.
- Quirk: Release took time. Docs, audits, sign-off. It felt slow, but I get it. Safety first.
- Culture: Mission-driven. Heads-down. Fewer jokes, but deeper talks.
Pay then: About 150k base, decent equity. Good health benefits, which made sense.
Job 3: A trading shop near the Board of Trade
I can’t say which one, but you’ve seen their hoodies. I worked on signals for short horizons. Mostly Python. Some C++ from the old guard. Data lived on S3 with Parquet. I tested features on a tiny cluster and prayed I didn’t introduce look-ahead.
- Win: I shipped a feature that helped cut slippage on certain instruments. Very narrow, very fun.
- Day to day: Unit tests or it didn’t happen. Backtest or it didn’t ship. Latency talk over coffee.
- Quirk: Stress. Big numbers. Low ego, high bar. You learn or you leave.
- Culture: Smart, quiet, kind of secret. No one overshared. Results spoke.
Pay then: 190k base, bonus varied. The jump was real, but so was the grind.
What the market looks like from the ground
- Where the jobs live: West Loop, Fulton Market, River North, The Loop. Google’s there, too. Meta has a small but mighty analytics pod—I walked through their loop in my first-person Meta data science interview review. JPMorgan also keeps data scientists downtown; wrangling their vendor Aditi turned into its own honest take. So are Sprout Social, Morningstar, and G2. Out in the burbs? Walgreens, Allstate, Discover.
- Common stacks: SQL (Snowflake, Redshift, BigQuery), Python, dbt, Databricks, Tableau or Power BI. Some teams love Looker. Some still love Excel. Honestly, both work.
- Titles you’ll see: Data Analyst, Analytics Engineer, Data Scientist, ML Engineer, Decision Scientist. Same soup, new bowls. Read the JD.
Industry-wide reports hint that the demand curve isn’t flattening anytime soon; the latest data scientist job outlook through 2025 underscores a continued double-digit growth trajectory, and independent coverage of data-science and AI hiring trends in Chicago corroborates that the Windy City will keep needing fresh talent.
Pay ranges I’ve seen (ballpark, base):
- Data Analyst: 70k–105k
- Data Scientist: 110k–165k
- Senior DS/ML: 150k–220k
- Quant/Trading DS: 180k–300k+ (bonus swings)
For a concrete salary walkthrough, my first-person look at the data science analyst comp at Copart shows how these numbers play out in practice.
Commute, seasons, and small life stuff
The CTA is fine. The Blue Line got me to work fast. Metra helps if you’re out in Evanston or Oak Park. Winters are real. Buy boots. Summers? Street fests, rooftops, lakefront runs. I once wrote a whole feature spec on a patio in Fulton Market. Best meeting I ever had.
Hybrid is common now. Two or three days in office. Teams vary. Finance leans more in-office. Health and e-comm are mixed.
Sometimes after back-to-back stand-ups and late-night Airflow war stories you just want a totally different conversation that isn’t about data at all. For a no-strings, quick-connect option, check out GayChat’s Omegle alternative where you can jump into LGBTQ-friendly random chats, blow off steam, and come back to your notebook feeling recharged. If a sprint review ever lands you on a last-minute flight to the L.A. area and you’d rather skip the endless swipe game for some in-person downtime, the Skip The Games Bellflower playbook lays out straight-to-the-point local meet-up spots and safety tips so you can maximize fun and minimize hassle before the next morning’s stand-up.
The interviews that stood out
- A/B test at an e-comm startup: They asked about guardrails and novelty effects. I suggested CUPED and a ramp plan. They smiled. I got an offer.
- Forecast case at a logistics shop: I skipped the fancy LSTM and used Prophet as a baseline, then XGBoost with holiday flags. Clear plots beat clever tricks.
- SQL speed round at a media company: Window functions saved me. So did named CTEs. Clean reads well.
Tip: Bring one story for failure. Mine was a feature store change that broke a DAG. I owned it, wrote a runbook, and added alerts. They liked that more than any win.
The good, the bad, the windy
Pros:
- Lots of fields: food, health, finance, logistics, research
- Strong meetups and kind folks
- Pay holds up; cost of living isn’t coastal-wild
- Real problems, real data, real stakes
Cons:
- Winters test your soul
- Some teams still fight over data ownership
- Legacy stacks pop up; migration pains are real
- Trading culture isn’t for everyone
If you’re starting fresh
- Ship small, real projects: A local transit forecast, a Bulls shot chart, or a snow day predictor. Keep it clean and honest.
- Join ChiPy or PyData Chicago. Ask one question. That’s enough.
- Learn dbt and a cloud warehouse. That combo opens doors here.
- Take a weekly peek at vhfdx.net; the forum’s eclectic threads surface off-beat tools, signal-processing analogies, and occasionally hidden Midwest job