17,333 active roles, straight from company career pages.
The data and AI job market is in flux. What used to be a clear distinction between "data analyst," "data engineer," and "machine learning engineer" is blurring fast. Companies are hiring across all three roles, but the titles themselves are evolving, the skill stacks are overlapping, and the tools that show up in job descriptions reveal what employers actually need right now.
JobFerret is tracking 17,333 active data and AI roles globally. This isn't a stable category anymore. Some positions that would have been labeled "analyst" five years ago now demand SQL and Python at the level you'd expect from an engineer. Some "ML engineer" roles are really applied analytics positions. And some "data engineer" openings are looking for people who can do feature engineering, model validation, and stakeholder reporting in equal measure.
The hiring wave is real, and it's reshaping how companies think about their data teams. Toloka Annotators leads the hiring pack with 212 open roles, followed by SpaceX (129), Turner Townsend and Accenture Federal Services (101 each), Databricks (99), and Nebius (89). These aren't all pure tech companies either. That diversity of employers signals how far data and AI have penetrated across industries.
Geography matters less than it used to. Among roles with a stated location, the United States dominates with 95 listings, followed by Remote (65), Washington, DC (52), San Francisco (50), and London (39). But 422 listings have no location specified at all, which reflects the reality that remote work is now baked into data hiring. Our data shows 28% of these roles are explicitly remote, but the true number is likely higher when you account for unlabeled positions.
The tooling story is where the real insight lives. Job descriptions reveal what's actually being used in production, not what's trendy on Twitter. The tools that appear most frequently across these 17,333 listings tell you what you should learn if you want to be competitive, whether you're positioning yourself as an analyst, engineer, or something in between.
If you're exploring a move into data or AI work, the key is to understand that the old job titles are descriptive labels on a spectrum now, not hard boundaries. An analyst role at a growth-stage startup might require the technical depth of an engineer. An engineer role at a large enterprise might involve as much stakeholder communication as analysis. The companies hiring most aggressively are the ones that have already figured this out.
The lines are blurring. Analysts increasingly write production code. Engineers increasingly own analytics and metrics. ML engineers are expected to understand data pipelines and business context. The best way to understand what a specific role requires is to look at the actual job description, the tools listed, and the team structure.
28% of roles are explicitly listed as remote. Among roles with a stated location, Remote ranks as the third most common location after the United States and Washington, DC. Many unlabeled positions are likely remote-friendly as well.
Toloka Annotators (212), SpaceX (129), Turner Townsend (101), Accenture Federal Services (101), Databricks (99), and Nebius (89) are leading the hiring effort across the 17,333 active roles we're tracking.