Charlie Hildbold More of my work

What rideshare really pays

I reconciled three and a half years of my own driving data to answer one question: per hour of my time, what does this job actually pay? The app makes it look like about $95. The real number is $41.61.

Trips
25,549
Hours on the clock
7,876
Miles
158,940
Net earnings
$327,729
Days driven
1,040
Raw data
415,000+ rows

The app's number leaves out half the job

Uber shows drivers what they earned while a rider was in the car. That skips the drive to the pickup, the wait in the airport lot, the empty drive home from a long trip, and the dead time between requests. All of that is still work, and none of it is paid.

I ran Gridwise the whole time, which logs every hour I was on the clock. Divide what I actually netted by those hours and the picture changes.

$95/hr
What Uber's on-trip numbers imply. Gross fares, only the minutes a rider was in the car.
$41.61/hr
What I netted after platform fees, across all 7,876 hours on the clock.

The app's number is about 2.3 times too high. That's not a lie, it's measuring the wrong thing. If you only ever look at the app, you'll badly overestimate what an hour of this work is worth.

Getting four sources to agree

No single export told the whole story, and they didn't agree with each other. Most of the work was making them line up.

  1. Pull everythingUber's trip history (46,548 rows) and payment ledger (368,869 line items), Lyft's rides, sessions and payouts, and Gridwise's daily earnings, hours and mileage. Six files, 415,000+ rows.
  2. Reconcile the ledgersGridwise's earnings totals matched Uber's payment ledger to within $1,000 over three years. The biggest single gap came down to one day.
  3. Explain the outliersMy best day on paper was February 28, 2024: $1,920.98 on four rides. It turned out to be a $1,868.16 EV settlement Uber paid outside its normal earnings records. It's real money, so it stays in the totals, and it's flagged on every chart.
  4. Use the right unitEverything is per hour on the clock, per mile and per trip, net of platform fees. Not per hour with a rider in the car.
YearDaysTripsNet earningsHoursMilesPer hourIRS mileage deduction
20233067,565$94,2772,242.644,730$42.04$29,299
20243127,595$97,9062,283.946,882$42.87$31,411
20253127,718$98,7532,462.549,754$40.10$34,828
2026 so far1102,671$36,794887.417,573$41.46$12,741
Total1,04025,549$327,7297,876.4158,940$41.61$108,278

The deduction column is miles driven times the IRS standard mileage rate for that year. Other per-unit numbers for the whole period: $2.06 per mile, $12.83 per trip, 3.24 rides per hour, 20.2 mph average.

Steady, month after month

Outside the settlement month, every full month landed between about $6,500 and $9,600. The hourly rate barely moved year to year: $42.04, $42.87, $40.10, and $41.46 so far in 2026. The job is consistent. The question is how to make it pay more.

Net earnings by month
$0$4k$8k$12k20232024EV settlement20252026
January 2023 to May 2026. Red: February 2024, which includes the $1,868 EV settlement. The last bar is May 1 to 9 only.

When I drive matters more than how much

Saturday paid $53.24 an hour. Monday paid $33.11. Same car, same driver, same city, and 61% more for every hour. Per day the gap is just as wide: $453 on an average Saturday against $272 on a Tuesday. It's the biggest lever in the whole dataset.

Net earnings per hour on the clock, by day
$0$20$40$60Mon$33.11TueWedThuFriSat$53.24Sun
All 1,040 days driven. Hover any bar for days driven and the per-day average.

The hours tell the same story. 97% of my Uber trips started between 3 PM and midnight, heaviest on Friday and Saturday nights. Mornings are basically empty. That's my schedule, not the market's, so it says as much about how I work as where the demand is.

Completed Uber trips by hour and day
Fewer tripsMore trips
24,744 Uber trips with a recorded start time. Hover a cell for the count.

Uber's cut went up in 2026

From Uber's per-line-item payment ledger, I divided Uber's total commission by everything riders paid. For three years it held right around 40%. In 2026 so far it's 44%. My hourly rate held steady through it, which means I'm working smarter to stay even, not getting ahead.

Share of rider payments Uber kept
0%10%20%30%40%50%40.2%202339.3%202439.9%202544%2026 so far
Commission divided by total rider payments, from 368,869 Uber payment line items. 2026 runs through May 9.

Surge is a bonus, not a plan

71% of my Uber trips had no surge at all, and those base-price trips are the backbone of the income. Surge pays well when it shows up. The 248 trips at 3x or higher averaged $32.19, about 2.2 times a normal $14.67 fare. But it's rare, and chasing it means driving empty to get there.

Average fare by surge level
$0$10$20$30$40No surge$14.671.0–1.5×$16.511.5–2.0×$20.882.0–3.0×$25.243.0×+$32.19
24,744 Uber trips with surge data. Hover a bar for the trip count.

Three smaller findings

2.7x

Airport runs pay, and I skip them

Pittsburgh International trips averaged $42.69 against $15.76 for a city trip. Only 139 of my trips were airport runs, 0.6%. Doubling that would add roughly $1,100 a year, before counting the longer drive and the empty trip back.

39.1%

Riders tip when the ride is good

Riders tipped on 39.1% of my trips, $35,911 in total. That's money that only shows up when the ride goes right.

97%

Nearly all Uber

97% of my trips were Uber. Lyft paid almost the same per trip, $12.35 against $12.77, across 768 rides. It's a real backup option I've barely used.

Why I did this

Most portfolio projects start with a clean dataset somebody else prepared. This one started with six files that disagreed, an outlier day that looked like an error, and a headline number that was technically true and still wrong. Sorting that out is most of what analytics work is in a front office or anywhere else: find the right source, reconcile what doesn't match, pick the unit that answers the real question, and explain it so someone can act on it.

About the data

Want to talk about it?

I'm looking for a front office or analytics role in the Pittsburgh area. I spent 15 years running minor league sports businesses before I went back for my master's in data analytics.