Machine Learning Jobs
Explore 1,328 open machine learning jobs at fast-growing tech, startup, and AI companies hiring right now. Train and ship models: features, evaluation, and getting them into production.
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The data career ladder
Data careers often start as an analyst answering business questions with SQL and dashboards, then grow into a data scientist who builds models and runs real experiments. Senior is where you own the hard problems end to end and start setting the bar for how the team works with data.
Above senior the path splits. You can stay hands-on as a Staff or Principal Data Scientist, owning the most important models and the toughest analysis. Or you can move into leadership as a Data Science Manager, Director, and VP of Data, building the team and the data strategy behind it.
The pivot point: Senior Data Scientist
Senior is where the path widens. If you love the modeling and the analysis, the Staff and Principal track keeps you hands-on with the hardest problems. If you would rather build a data team and set strategy, the Manager and Director path is yours. The two tracks usually pay the same at each level.
What changes as you climb
- The line between analyst, data scientist, and ML engineer is blurry and varies by company. Titles matter less than what you actually build.
- The IC track (Staff, Principal) is strong at larger companies with real data orgs. Small teams hire one generalist who does a bit of everything.
- Machine learning and experimentation skills separate the senior levels. Pure dashboarding tends to cap out earlier.
- A lot of data leaders came up through analytics or research rather than engineering.
Where everyone starts
Individual Contributor
Illustrative titles and pay. Real ladders vary by company. Counts are live open roles matching each level.
IC vs. Management, side by side
| Individual Contributor | Management | |
|---|---|---|
| Core work | The hardest models, experiments, and analysis | Hiring, roadmap, and the data strategy |
| Judged on | The impact of the work you ship | The output and growth of the data team |
| A normal day | SQL, modeling, experiments, and writing up results | 1:1s, planning, stakeholders, and hiring |
| Scope grows by | The reach of your models and analysis | The size of the team you lead |
Kinds of data
Data is a wide field. Most people specialize in one area over time, and the day-to-day work looks different in each. Here are the main tracks and what they cover.
Data jobs: common questions
What does a data scientist do?
A data scientist finds the signal in a company's data using statistics, experiments, and models, then turns it into decisions the business can act on. That runs from a data analyst answering questions with SQL and dashboards to a scientist building and shipping models. The exact focus depends on the team, from analytics to machine learning to research.
How do I become a data scientist?
Common paths in are a quantitative degree, a bootcamp, or moving up from a data analyst role after showing you can model and experiment. What teams look for is the ability to go from a messy question to a defensible answer: SQL, statistics, and the judgment to know when a result is real. A portfolio of real analysis helps.
What is the data career path?
The usual path runs analyst to data scientist to senior, owning harder problems at each step. Above senior it splits: you can stay hands-on as a Staff or Principal Data Scientist on the most important models, or move into leadership as a Data Science Manager, Director, and VP of Data building the team and strategy.
Which skills and tools should a data scientist learn?
SQL and Python are the floor, with statistics and data modeling right behind. Machine learning and experimentation are what raise the ceiling toward senior. On tools, most teams work in a warehouse like Snowflake or BigQuery with dbt, notebooks in Python, and BI in Looker or Tableau. Experimentation tools like Statsig show up on product-facing teams.
Do you need a degree to work in data?
It helps more here than in some fields, since the statistics and modeling have real depth, and many data scientists have a quantitative degree. That said, plenty came through bootcamps or self-study plus a strong portfolio. The bar at hiring time is whether you can actually do the analysis, not the credential.
How much do data scientists make?
Pay ranges by level, location, and company. In the US a mid-level data scientist commonly lands in the range shown on this page, Staff and Principal roles go higher, and leadership adds equity on top of base at larger companies. The salary bands here are illustrative; the open roles listed are real postings.
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Data jobs by location
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