I’m Kayla Sox. I build models, clean messy data, and argue with dashboards for a living. I’ve also worked with a bunch of data science recruiters. Some helped me change my pay and my peace. Some wasted my time. Here’s what actually happened to me, names and all.
If you’re after an extended collection of war stories—including a few not covered here—check out my separate deep-dive on data science recruiters.
I’ve used recruiters in 2020, 2022, and again in 2024. Austin and remote roles. Product analytics, ML, and a little data engineering. Different seasons, different needs. I haven’t spent a full stint in California yet, but I did survey the scene and detail what data science roles actually look like across Santa Monica, Culver City, and downtown in this Los Angeles jobs breakdown.
The kinds of recruiters I met
- Boutique data folks (Harnham, Burtch Works, Selby Jennings for quant)
- Big general shops (think the big national firms; fine for volume, hit-or-miss for fit)
- In-house recruiters (company recruiters who reach out on LinkedIn)
- Contract networks (Toptal for short stints and niche skills)
They each have a lane. And no, they don’t all drive well in your lane.
Story 1: Harnham got me a real bump (Austin, 2022)
I was a Senior Data Analyst in retail. SQL, Python, and A/B tests were my daily bread. My Harnham recruiter, Anna, called me on a Friday. She knew the hiring manager at a big e-commerce brand in Austin. The role sat between product and data. Less report churn, more experiment design. My jam.
What stood out:
- She sent a one-pager on the case study. Clear scope: write SQL to pull cohorts, then explain test power in plain English.
- We did a 30-minute mock call. She nudged me to tell short, story-first answers. That storytelling approach was drilled into me during the fellowship—see my candid review of Insight Data Science for the full download.
Result:
- Offer went from $130k base to $145k base, plus a $10k sign-on. I would’ve aimed lower. She pushed high and held firm.
- Time to offer: 3 weeks. Smooth. No mystery rounds.
Small note: onboarding had messy dashboards (don’t they all?). But the role itself matched the brief—messy dashboards actually reminded me of the spaghetti I helped untangle during my Costco data science internship. That almost never happens. It did here.
Story 2: Burtch Works saved me from a bad fit (Remote, 2024)
I was flirting with a “Marketing Data Scientist” role. The title said DS. The work smelled like heavy stakeholder reporting. My Burtch Works recruiter, Marco, sent me their salary report and a list of common task splits. He asked, “Do you want 70% reporting?” I said no. He pulled me back from the ledge.
We still ran the process:
- Two rounds. Light take-home: one slide on test design for a pricing change.
- I shared how I’d measure lift and handle spillover.
They liked me. I passed. Then I walked. Marco didn’t guilt trip me. He set up two fresh leads in a week. That’s rare grace.
Bonus tip from him that worked: move “Impact” bullets to the top. Example: “Cut model run time from 90 min to 18 min by caching and pruning features.” Short. Punchy. Helped.
Story 3: A big general firm burned time (2020)
I won’t name them. It was one of the big national shops. Nice people. But they kept sending me “data roles” that were not data roles. One was 100% dashboard build with no SQL access. Another wanted a 12-hour unpaid take-home plus a live whiteboard. For an “Analyst II.” You can guess how I felt.
I set a hard rule: no take-homes over 3 hours unless paid. They ghosted me after that. Honestly, that told me enough.
Story 4: In-house recruiter at Capital One kept it clean (2021)
A Capital One recruiter messaged me on LinkedIn. The job was on a credit risk team. Think: feature work, model monitoring, and clean MLOps for once. Three rounds:
- SQL screen with window functions
- Business case on model drift
- Panel chat on teamwork and checks
Clear timeline. Clear feedback. Offer was fair. I didn’t move forward due to location at the time, but I still use their case prompts to coach friends. It felt adult.
Story 5: Selby Jennings for quant roles (NYC, 2023)
I got curious about a more quant path. Selby Jennings lined up a fund. Heavy stats. More Python than slides. Quick loop:
- Take-home on factor signals (2 hours)
- Live math (light calc, solid logic)
- Chat with a PM
New York's intensity never really surprised me—I’d already tasted it during my data science internship in the city. It was real quant, but the team wanted very late nights. I passed. Still, they were upfront about comp ranges and hours. No bait and switch.
Story 6: A Toptal contract kept my skills fresh (Remote, 2023)
I did a 3-month NLP contract through Toptal. I had to pass their screen first. Short project set up and clean scope docs. Got paid on time. It wasn’t a full-time hire, but it filled a gap and proved a point on text pipelines. Also, more variety keeps me happy.
The good stuff recruiters bring
- They know who’s actually hiring, not just posting.
- They prep you for the weird bits: case style, stack quirks, manager’s pet topics.
- They negotiate better than most of us. It’s their sport.
- They can save you from title traps (Analyst vs Scientist vs “Data Something”).
Before I trust any new service—whether it’s a recruiter or a niche social app—I read an unvarnished breakdown first; for instance, this candid SnapSext review walks through the app’s features, pricing, and safety considerations, giving you the clarity to decide if it’s worth your time. Likewise, when a spring-break conference lands me near Florida’s Emerald Coast, I do some due diligence before mingling with the local scene; seasoned travelers point to the street-smart rundown in this Skip The Games Fort Walton Beach guide that highlights which listings feel real, what etiquette keeps the night low-stress, and why a quick read can save you both time and hassle.
Spotting the perfect role can feel a lot like tuning a radio: when you dial into the right frequency—think of how enthusiasts track signals on vhfdx.net—the opportunities come through loud and clear.
The stuff that tests your patience
- Role inflation. “Data Scientist” that’s 90% reporting.
- Huge unpaid take-homes. Hard no for me now.
- Ghosting when you set boundaries. Tells you all you need to know.
- Vague ranges. If they won’t share comp bands, I pause.
How I work with recruiters now
This is my little script. It keeps things clean.
- I ask for the exact tech stack, team size, and % mix of work (experiments, modeling, ETL, slides).
- I ask for the case style and sample questions.
- I give a salary range and a floor. I stick to it.
- I ask for the real job title and level so I can sanity-check pay bands.
- I set limits on take-home time. I’ll do 2–3 hours. Beyond that, pay me or pass.
You know what? Most good recruiters like these rules. It helps them help you.
Who I’d call again (and why)
- Harnham: great for product analytics and DS at real tech and e-comm shops. Sharp prep, clean briefs—and you can scan the Glassdoor reviews of Harnham for employee takes.
- Burtch Works: honest market reads, useful reports, and they don’t push if the fit is off.
- Selby Jennings: serious shot at quant or HFT tracks. Expect math. Expect candor.
- In-house recruiters at places like Capital One: clear process, practical cases, decent comms.
- Toptal (for contracts): screening first, but then quick, paid project work that keeps skills warm.
Caution with the big national generalists. Some are fine. Some spray and pray. If they can’t answer basic questions, I bow out.
What changed for me
- Pay: I