$110,000
median · per year, annualized USD
$189,000
median · per year, annualized USD
Senior Data Analyst, Credit Risk
Senior Data Scientist / Analyst, Institutional
Senior Data Scientist / Analyst, Growth
Senior Data Scientist / Analyst, Markets
Senior Data Scientist / Analyst, Finance
Senior Data Scientist / Analyst, Product
Software Engineer, Macro Quant Analytics Technology
Senior Data Scientist / Analyst, Fraud & Risk
Trust & Safety Ads Operations Analyst, Data
Staff Analytics Analyst, Full Stack (Revenue)
Senior Machine Learning Engineer, DevOps/SRE
Staff Machine Learning Operations Engineer
Staff Engineer - ML Infra / MLOps
Staff ML Ops Engineer
Lead Software Platform Engineer, MLOps
Senior ML Ops Engineer (Machine Learning Infrastructure)
MLOps Engineer
MLOps Engineer (JAX, PyTorch, Pallas/Triton)
ML Ops Infrastructure Engineer
Senior Machine Learning Engineer, Operations Research
| Company | Median | Roles |
|---|---|---|
Air Apps
|
$65,173 | 10 |
Accenture Federal Services
|
$105,000 | 8 |
| SE Sezzle | $57,600 | 7 |
Polymarket
|
$250,000 | 6 |
Lyft
|
$90,804 | 6 |
Klaviyo
|
$155,000 | 5 |
Affirm
|
$189,000 | 4 |
|
|
$181,800 | 4 |
| Company | Median | Roles |
|---|---|---|
Torc Robotics
|
$142,650 | 2 |
Garner Health
|
$324,500 | 1 |
Roku
|
$254,875 | 1 |
Quince
|
$251,500 | 1 |
LVT
|
$242,650 | 1 |
TetraScience
|
$235,000 | 1 |
| AM Atomic Machines | $225,000 | 1 |
Instacart
|
$212,750 | 1 |
| Location | Median | Roles |
|---|---|---|
| Seattle | $159,500 | 11 |
| Boston | $150,000 | 15 |
| New York | $140,000 | 77 |
| San Francisco | $140,000 | 30 |
| Los Angeles | $134,000 | 6 |
| Austin | $121,800 | 7 |
| Denver | $115,000 | 11 |
| Singapore | $114,400 | 3 |
| Atlanta | $102,500 | 3 |
| Chicago | $96,511 | 14 |
The median for Data Analyst is $110,000 per year (typically $82,500–$154,500), versus $189,000 for MLOps ($152,900–$225,000). That puts MLOps about 72% ahead at the median.
Both figures are computed live from active listings on JobsRadar and normalized to annualized USD, so Data Analyst and MLOps are compared on equal terms regardless of the currency each role was originally posted in. Only roles that publish a salary range feed the medians; numbers refresh every few hours as new roles post and older ones close, so this comparison reflects the market right now rather than a fixed survey. Use it as a directional benchmark when weighing one path against the other.