Quick note on what I’ll cover:
- What I did day to day
- What I got paid and why
- Real project examples
- Perks, pain points, and who this job fits
The short answer
I worked as a Data Science Analyst at Copart in Dallas. I liked the work. The pay felt fair for Dallas. Not crazy high. Not low. I unpack the whole offer in my honest take on the Data Science Analyst salary at Copart for anyone who wants every penny and perk spelled out.
Where I sat and what I worked on
I sat at HQ near the Dallas North Tollway. The building was cold, and the coffee machine worked hard. My team was six people: a manager, three analysts, one data engineer, and me. We looked at car auction data. Think lots, bids, fees, and sale times.
Here’s what I did most days:
- Wrote SQL to pull big tables. Sometimes 100 million rows big.
- Cleaned VINs, dates, and weird codes. You know that feeling when one field ruins a whole query? Yeah, that.
- Built simple models in Python. Mostly price and time-to-sale.
- Made reports in Power BI for branch leaders and ops.
- Sat in stand-ups. Short and brisk. Sometimes funny, sometimes not.
Tools I used: SQL Server, Python (Pandas, scikit-learn), Power BI, and Git. We stored stuff in Azure. Nothing too fancy. Solid stack though.
What I got paid (my real numbers)
This is my actual offer sheet from 2023 in Dallas:
- Base salary: $104,000
- Annual bonus target: 10% (paid in March for the prior year)
- Sign-on: $5,000 (one-time)
- 401(k) match: 4% for me
- ESPP: yes, with a small discount
My bonus paid out at 9% that year. It hit a month later than the date on the doc, which was annoying, but it came. If you like to cross-check numbers, Glassdoor’s Copart data science salary page lists a range that lines up pretty closely with what I saw inside.
Some teammates got 0–12% based on targets and team goals.
I also saw two teammates’ pay:
- A Senior Data Analyst in the same group: $128,000 base, 12% bonus target.
- A Data Science Analyst who worked mostly remote in Phoenix: $115,000 base, 8% bonus target.
These are single data points, not a rule. But they matched what recruiters told me: most Data Science Analysts were in the $95k–$120k base range in DFW, with a small bonus.
Real projects I shipped
Let me explain a few that mattered.
-
Price hint model for auctions
We built a price hint for certain lots to help with reserve settings. It wasn’t magic. It used year/make/model, damage type, odometer, title brand, region, and season. We tried XGBoost and a simple ridge model. The ridge model won. Why? Faster and stable. It improved reserve accuracy by about 6% across a test group. That cut back-and-forth re-listing for those lots. Ops was happy. I was too. -
Time-to-sale dashboard for branches
I made a Power BI report that showed how long cars sat before sale. We broke it down by yard, day of week, and title delay. This helped one busy yard near Houston cut average time by 1.3 days. The fix? Better intake photos and earlier title checks. Simple things, big wins. -
Bid anomaly alerts
We flagged odd bid jumps late in auctions. Not drama. Just alerts. I set a rolling z-score, then we tuned it with a few business rules. It fed into a small review queue. The fraud team called it “useful but chatty.” Fair. -
VIN decode cleaning
Not fun, but real. We had a batch decode job that missed edge cases. I wrote a short Python script to fix common decode gaps, then logged outliers. This raised match rates by a bit (around 2%), which kept our price model from wobbling.
How I asked for more money
I asked for $115k base. They said $104k base and a 10% bonus target. I asked for more sign-on since the base didn’t budge. They met me at $5k sign-on and a faster review at month nine. The review came on time. My raise was 4%. Not huge, but steady.
My tip: bring local comps from DFW. Also bring a work sample. I showed a quick Power BI mock with fake auction data. It helped.
The good stuff
- Clear problems. Cars must sell, and time matters.
- Solid tools. No wild tech stack, which I liked.
- Nice managers. My boss shielded us during quarter-end crunch.
- Cost of living in Dallas helped the salary feel bigger than it looked. A recent cost-of-living breakdown by Axios shows why Dallas can stretch a paycheck compared with the coasts.
The hard parts
- Data quirks. Title dates and damage codes can be messy.
- Bonus is small. It’s real, but don’t count on it for rent.
- Some days felt like pure reporting, not “data science.”
- Release cycles moved slow in ops-heavy teams.
Salary vs. life math
This is what I actually paid:
- Rent in Addison: $1,700 for a one-bed with a carport.
- Parking: free at HQ.
- Gas: not fun on Tollway weeks.
- After tax and 401(k), I could save a bit each month. Not a ton. But steady.
If you’re in San Jose or New York, this pay may feel low. And if Los Angeles is on your radar, check out my real take on data science jobs in Los Angeles for a side-by-side on comp and cost of living.
While we’re talking SoCal: if your career or weekend wanderings land you an hour northwest in beachy Oxnard and you’d rather line up no-strings meet-ups without the endless swipe fatigue, this quick rundown of local shortcuts via Skip The Games Oxnard can save you time by highlighting trusted spots, common red flags, and tips for staying discreet.
For a glimpse at the entry-level side of things in the Big Apple, here’s my data science internship in New York – a real review.
For a searchable set of first-hand salary breakdowns across dozens of tech roles, take a look at vhfdx.net before you walk into any negotiation.
One cultural note if you’re relocating to Dallas solo: finding a community outside of work helps keep the move from feeling isolating. If you’re LGBTQ+ and want an easy way to start meeting locals before you even land, hop into InstantChat’s gay chat rooms—you can swap tips on neighborhoods, discover weekend events, and build a support network that makes settling in smoother.
Who this job fits
- You like SQL more than shiny slides.
- You enjoy the mix: some modeling, lots of metrics.
- You want business impact you can see next week.
- You’re okay with edges that aren’t perfect.
If you want heavy research or deep ML, this might feel light. If you like fixing real pipeline bumps and showing results fast, it’s great.
Final verdict and a small nudge
Would I take it again? Yes, for Dallas. I grew a lot. I learned how auctions breathe. I learned how tiny fixes move big numbers. The money was fair, and the work was real.
If you get an offer like mine, ask for:
- A sign-on if base stalls
- A review at 6–9 months in writing
- A clear bonus target and payout history
- Clarity on tools and data access on day one
And bring a small demo. Even a simple price chart by region. You know what? That little touch says, “I can ship.” Which, in this job, is what counts.