I’m Kayla. I did the Insight Data Science Fellows Program in San Francisco back in 2018. I was finishing a PhD, tired of plotting error bars at 2 a.m., and I wanted a real job shipping stuff. I’d heard whispers: “Insight helps you land data roles fast.” I was curious, a little scared, and very broke. So I went. For a third-party breakdown of what the fellowship offers, you can skim this Pathrise review of Insight Data Science.
The chatter reminded me of how fierce the market can be for data science jobs in Los Angeles, too—everyone wants a shortcut.
Was it worth it? Short answer: mostly yes, with some sharp edges.
What Insight Felt Like Day to Day
It ran for about seven weeks. My cohort met in a big shared space near SoMa. Whiteboards on every wall. Cold brew in the corner. We did fast stand-ups each morning, then worked like crazy.
- Weeks 1–4: build a project you can demo.
- Weeks 5–7: interviews, talks, and a thing they called “Interview Day,” which felt like startup speed dating.
No stipend. Rent in SF hurt. That’s the part no one likes to say out loud. But I’ll say it: plan your money.
My Project: Predicting Brunch Wait Times
I built a simple tool to guess wait times at busy brunch spots in SF. Why brunch? Because people get hangry. And a good demo needs a story. Also, I love pancakes.
Here’s what I did, in plain speak:
- Data: I pulled Yelp hours and foot traffic signals, mixed in weather, and scraped a few menu pages. I learned how messy web data can be. Broken HTML. Odd time zones. A restaurant that changed its name midweek—cute.
- Model: I used XGBoost (a tree model that handles weird patterns well). My target was minutes of wait time. I kept a baseline with a moving average. In tests, my mean error got down to around 7–8 minutes for popular spots.
- Pipeline: I used Python, pandas, and scikit-learn. Airflow ran the daily jobs (think: a tool that pushes tasks on a schedule). Data in PostgreSQL. I cached hot spots in Redis so the app felt snappy.
- App: A tiny Flask service on AWS EC2, wrapped in Docker. Front end was plain, with a simple chart (D3) and a map. It wasn’t pretty, but it worked.
- Demo: Three minutes. One story. One clear plot. One live click. That was the whole point.
You know what? Shipping that tiny thing taught me more than any long lab project. Code that runs beats ideas that don’t. If you want to see another example of a scrappy project that just works, take a quick look at VHF DX—it’s literally one page that surfaces real-time amateur radio propagation data and nothing more.
Real Things I Struggled With
- My first model cheated. It learned the day of week in a silly way and guessed “long waits” on Sundays no matter what. I caught it after a mentor asked, “What happens if it rains?” Oof.
- The scraper broke when a site added a cookie banner. I swapped to the Yelp Fusion API to keep it stable.
- Cold start problem: new restaurants had no history. I used a simple trick—median by cuisine and neighborhood—and called it a day. Not perfect, but clear.
See the theme? Keep it simple. Explain it like you would to a friend.
Mentors, Talks, And The Interview Push
Insight brought in folks from places like Airbnb, Stitch Fix, and LinkedIn. My main mentor worked at Pinterest. He was kind, but also blunt. He’d ask, “What’s your metric?” every time. I now hear that in my sleep.
If you're earlier in your journey, a structured program like a data science internship in New York can provide similar mentorship with a gentler runway.
We had mock interviews too. SQL on the board (window functions, joins), ML basics (bias vs. variance), and “product sense” chats like, “How would you measure a rec system?” I got better at saying, “I’d run an A/B test and track click-through, save rate, and churn,” without rambling.
Interview Day felt wild. Ten quick chats. That sort of real-world “speed-dating” energy got me thinking about how we now swipe for matches online; if you’re curious how that translates inside a popular dating platform, take a look at this no-fluff Bumble review—it walks through the app’s user experience, match quality, and whether upgrading is worth the money. Likewise, if you ever find yourself in Montana and want a ground-level look at how casual meet-ups work beyond standard swipe apps, check out the Skip the Games Helena overview—it breaks down local etiquette, safety pointers, and cost expectations so you can decide if arranging an in-person meetup fits your comfort zone. Lots of smiles. One awkward moment where my laptop died mid demo. I kept calm and sketched the pipeline on a sticky note. Weirdly, that helped.
I ended up with a few onsites. I got two offers. I chose a mid-size company with a real data team and friendly vibes. The brunch app story stuck in their heads. Food wins hearts, I guess.
What I Liked
- Fast feedback: I got code and talk feedback daily. It stung sometimes, but it helped.
- Real gear: AWS, Docker, Airflow, Git, scikit-learn, XGBoost. Tools you’ll use at work.
- Clear pitch: They forced me to make a tight demo. Not a thesis. A product.
- Alumni: People picked up the phone. Folks shared old interview questions and honest notes. That mattered.
What I Didn’t Love
- Cost of living: No stipend. SF rent ate my savings. No joke.
- Pace: It’s a sprint. If you’re not ready to build day one, it’s rough.
- Luck factor: Company matches can feel random. Your project topic helps, but timing is real.
- Not a starter course: If you’ve never coded, it’s not the place to begin.
If you’re still on the fence, it helps to read uncensored Glassdoor feedback from past Insight fellows to see how their pros and cons stack up against mine.
Who It’s Good For
- PhD or master’s folks who can code and want to switch paths.
- Bootcamp grads who already shipped small projects and want polish.
- People who like building fast and talking to strangers about it.
Who should skip? Total beginners. Also, if you can’t pause work or can’t cover living costs, the stress might be too high.
A Few Tips I Wish I Had
- Pick a project you can explain in one breath. “Guess brunch wait times” beat my first idea (“graph neural nets for protein maps”). Keep it demo-friendly.
- Show the baseline first. Prove your fancy model beats something simple.
- Track one main metric. Say it out loud. Put it on a slide. Repeat it.
- Make a one-pager. Problem, data, method, result, next steps. Busy folks love one-pagers.
- Rehearse the three-minute talk ten times. Then two more. Timing is a skill.
Did It Change My Career?
Yes. Not magic. But real. Insight gave me a focused runway, a polished demo, and a nudge into rooms I couldn’t reach alone. I still had to do the work—coding, testing, talking, and hearing “no” a few times. But I walked out with a job I liked and skills I still use.
Would I do it again? I would. I’d also pack more snacks and carry a spare charger.
Final Word
Insight Data Science was intense and messy and helpful. It pushed me to ship. It made me cut jargon and speak plain. It also cost me sleep and money for a while. Both can be true.
You can also weigh my story against another fellow’s honest perspective on Insight to see how experiences line up.
If you can handle a short, hard sprint, and you already have the basics, it might be your bridge. If not, try a smaller project first. Then circle back when you’re ready.
And hey—if you build a food app, send it my way. I’ll test it with pancakes.
—Kayla Sox