Quick note: I spent a summer on the data team at Costco in Issaquah, WA. If you're curious how other interns felt, the compiled Glassdoor reviews of Costco data analyst interns give a pretty candid snapshot. I wrote code, wrangled huge tables, and yes, I ate the $1.50 hot dog more than once. Here’s what actually happened.
If you want a second opinion on this same program from someone with a different background, I recommend skimming this real-talk recap of the Costco data science internship.
Fast gist
I liked it. I learned a lot. Some things moved slow. But the work touched real stores, real members, and real money. It felt serious, but also kind.
What I actually worked on
Project 1: Predict chicken demand
I helped forecast rotisserie chicken demand for a set of warehouses in the Southwest. We used Python with pandas and Prophet, plus some XGBoost tests. Data came from Snowflake tables with millions of rows. We pulled weekly sales, holiday flags, promos, weather, and even football game days for two cities. Why football? Sundays were weird.
- Problem: Sundays kept spiking. The old model under-shot by about 8% at one store in Phoenix. They kept running short at 5 p.m.
- What I did: Added a special-event feature for home NFL games; used a simple lag feature; tuned Prophet seasonality; set a Sunday cap. I know, nerdy.
- Result: The Sunday miss fell to about 2–3% for that store over four weeks. That meant fewer empty warmers and less waste too. The bakery folks sent a note. It made my week.
Project 2: Membership churn model
I built a churn score for members who might not renew. It was a basic tree model (XGBoost) with explainable features. Think visit gaps, department mix (gas, pharmacy, big-ticket), and months since last Costco Travel booking. We did feature checks in Databricks on Azure and tracked code in GitHub.
- Problem: Marketing wanted a smarter list. They had a broad email. It was cheap, but noisy.
- What I did: Trained on anonymized history. Kept it simple. We tested thresholds, looked at SHAP for fairness, and did a small A/B test with the CRM team.
- Result: The targeted email group showed about a 0.6% lift in renewals over four weeks. Not huge, but it paid for itself. And it was clean.
Reading how a peer navigated an urban setting helped me benchmark; their New York data science internship review highlights some sharp contrasts.
Day-to-day tools:
Python, SQL, Snowflake, Databricks notebooks, Tableau, Slack, Jira, GitHub, and sometimes Excel for quick checks. Airflow handled jobs. I had to file access tickets in ServiceNow and wait; security was strict, and that’s fair.
Work rhythm and people
Schedule: I did hybrid. Three days at HQ in Issaquah, two days at home. Hours were sane. Most days ran 9–5:30 with a real lunch. The campus was calm; it felt friendly, not flashy.
Mentors: I had one senior data scientist who cared. We did whiteboard time on Tuesdays. He made me write comments and tests. He said, “Make future you happy.” I liked that.
Code reviews: They were serious. Folks asked about leakage, null handling, and time windows. If you used the wrong join, someone noticed. It felt safe to ask “dumb” questions. I asked plenty.
Little moments: I wore a hairnet on a warehouse tour. The bakery oven hissed like a dragon. A forklift beeped and made me jump. I watched a member fill a cart with Kirkland pesto like it was gold. It was funny and sweet.
Outside of work, a few interns and I compared notes on Seattle’s social scene—turns out even data people like optimizing their off-hours interactions. If you’re curious about online avenues to meet new people, especially within specific cultural communities, check out this guide to the best Asian hookup sites for 2025 because it breaks down each platform’s user base, safety features, and cost structures so you can decide quickly which option fits your vibe. And if you ever find yourself on a field assignment near Holloman AFB or White Sands—Costco sometimes sends analysts to unexpected places—you might wonder how locals socialize in a tight-knit New Mexico town. In that case, here’s a candid walk-through of Skip the Games in Alamogordo that lays out what the platform is, how it works locally, and the red flags to watch for so you can stay safe while expanding your circle.
The good stuff
- Real impact: My work changed store orders and member emails. I could see it.
- Clean-ish data: Not perfect, but Snowflake tables were labeled and stable.
- Mentorship: People slowed down to teach. Test-first thinking stuck with me.
- Calm culture: No late-night crunch. No showboating. Just steady.
- Food perks: I won’t lie. That hot dog and a churro saved me on busy days.
The tough parts
- Access delays: My data access took 8 days. I watched a ticket sit. I learned patience.
- Guardrails: The laptop was locked down. Docker was limited. I had to ask for installs.
- Risk style: They like proof. New models need a trail of tests. It’s good, but it takes time.
- Meetings: Stand-ups were fine; some syncs felt long. I wished for more heads-down blocks.
- Scope drift: One task turned into three sub-tasks. I had to push back, kindly.
Here’s a tiny example: I tried a fresh feature store idea. It was neat, but security asked for a review. Then a second review. It took two weeks. We parked it. Honestly, I learned to write better tickets.
Pay, perks, and setup
My pay was a little over $40 an hour. I got a Costco membership, a laptop, and a nice desk setup on-site. For a sense of how other interns rate the compensation and culture, check out the Indeed intern reviews for Costco Wholesale. No badge flex. Just a friendly “good morning” from everyone. The view of the foothills on a clear day felt like a treat. Seattle summer helps; the berries at Pike Place? Unreal.
What surprised me
- The NFL feature mattered. I didn’t expect game day to change chicken that much.
- Pharmacy signals were strong for churn. Not in a spooky way—just steady patterns of visits.
- Tableau dashboards still rule the floor. People love a clear bar chart. Clean labels win.
What I wish I knew sooner
- SQL first: Fast joins and window functions save your week.
- Time series basics: Start simple. Prophet with good features beats messy deep stacks.
- Git hygiene: Small pull requests get merged. Big ones wait and wait.
- Ask for a store visit early: Seeing the floor makes your plots make sense.
- Keep a runbook: I tracked queries, table names, and gotchas in one doc; it saved me.
Some of those lessons echo what I noted after finishing the Insight Data Science program; process matters way more than fancy architectures.
One evening I dove into this concise primer on quirky seasonality patterns, and it sparked a neat idea for debugging the stubborn outliers in our chicken forecasts.
A short story from week 6
I shipped a forecast run that looked perfect. Then a store reported a mismatch. Did I mess up? Yes. I forgot a daylight savings flag for one region. My mentor and I wrote a one-liner fix and backfilled. We sent a note owning it. No blame. Just “we fixed it.” That trust stuck with me.
Who will love this internship
- You like calm, steady work that touches real stores.
- You care about clean code and careful releases.
- You enjoy simple models with smart features more than showy demos.
Who may not: If you want research-heavy deep learning or to push edgy tools every week, you might feel boxed in. It’s more craft than flash. You might instead vibe with the faster churn of West-coast startup scenes—I covered that pace in my real take on data science jobs in Los Angeles.
Would I do it again?
Yes. I’d go back. I’d bring better SQL snippets and a phone alarm that says “write tests.” I’d still get the hot dog. Maybe two.
Final verdict
Costco’s data science internship felt real and grounded. It wasn’t perfect. Access was slow, and change took time. But I learned, I shipped, and I saw impact. For me, that’s the good stuff. You know what? I left with more skill and