Data Scientist Jobs in 2026: 287 Open Roles and the Companies You're Probably Not Targeting
Most candidates treating the AI job market as a search for “ML Engineer” or “LLM Engineer” are walking past a larger category entirely.
Data Scientist is the second most common title in HireIndex’s current index of 1,789 open AI/ML roles across 748 companies — 287 roles as of May 18, 2026. That’s behind Machine Learning Engineer at 363 and ahead of everything else: Applied AI (157), AI Software Engineer (117), AI Research (107), and LLM Engineer (37).
The category is larger than most candidates expect. The companies hiring look nothing like the AI labs dominating industry coverage.
How many Data Scientist jobs are open right now?
287 explicitly-tagged Data Scientist roles in the current index — 16% of all tracked AI/ML openings. That’s up from 10.5% in April when the index covered 448 roles across 89 companies.
That proportional shift matters. The overall index grew roughly fourfold between April and May (448 → 1,789 roles) as HireIndex expanded its source coverage. Data Scientist outpaced the total: 10.5% → 16% of the market. Demand isn’t just tracking the overall AI hiring wave — it’s accelerating relative to the flashier titles getting the attention.
Here’s where 287 sits across the full title landscape:
| Title | Open Roles | Share of Index |
|---|---|---|
| Machine Learning Engineer | 363 | 20% |
| Data Scientist | 287 | 16% |
| Applied AI | 157 | 9% |
| AI Software Engineer | 117 | 7% |
| AI Research | 107 | 6% |
| LLM Engineer | 37 | 2% |
Data Scientist is not a niche. It’s a major category that lacks the cultural cachet of “AI Engineer” — which means lower competition-to-seat ratio for candidates paying attention to the actual numbers.
Who is actually hiring Data Scientists in 2026?
The short answer: enterprises, not AI labs.
The frontier AI organizations — OpenAI, Anthropic, Mistral, Cohere — concentrate their hiring under Research Scientist and Applied Researcher titles. The companies with the heaviest Data Scientist headcounts in the index skew toward financial services, tech platforms, and enterprise software.
Capital One, which leads the entire LLM Engineer category with 13 explicit roles, runs an even larger data science function underneath. Their 47 AI/ML positions in New York alone span risk modeling, fraud detection, credit decisioning, and personalization — a data science layer with direct revenue impact at institutional scale. This is not analytics work with a new name. It’s applied ML in production, on problems where errors cost billions.
Spotify’s 44 total AI/ML roles — including 11 in London — are anchored heavily in data science. Recommendation systems, audio understanding, ad auction optimization, and podcast discovery all run on teams of data scientists experimenting at scale and shipping results into products used by hundreds of millions of people.
Reddit (30 total AI/ML roles) maintains a serious data science function around community health, content ranking, and trust-and-safety. Databricks (53 roles) hires data scientists to work on and alongside the data platform used by most of the enterprise AI market. Scale AI (51 roles) needs data science to evaluate the evaluation: measuring quality, drift, and coverage across the datasets and model outputs it produces for clients.
None of these are the companies AI candidates typically target first. That’s the opportunity.
Where are Data Scientist jobs located?
The geographic pattern diverges from other AI roles in ways that matter for targeting.
New York leads for in-person Data Science work. The city’s concentration of financial services firms — Capital One’s 47 AI/ML positions, JPMorganChase’s significant AI footprint, and a dense network of hedge funds and insurance companies — makes New York the deepest in-person Data Scientist market in the dataset. New York’s AI roles skew 47% senior+, the highest concentration of any major city. Banks and financial institutions hire experienced data scientists, not growth candidates. If you have 5+ years of production experience, this is your market.
San Francisco is high-volume but structured differently. SF’s 129 AI/ML roles split at only 24% senior+ — the lowest of any major market. That’s primarily driven by OpenAI’s aggressive hiring across mid-level engineering positions. Data Scientist roles in SF, when they appear, tend to come from mature product companies with established functions rather than frontier labs. The competition-to-volume ratio is higher than New York because more people are targeting SF by default.
Remote is meaningful. Five companies control nearly 40% of all remote AI/ML hiring: Scale AI, Databricks, Reddit, Cresta, and Applied Intuition. Three of those five have significant Data Science functions that operate distributed. Databricks in particular runs a data science enablement layer that’s genuinely remote-accessible, not hybrid-in-disguise.
London adds a real second market. With 173 AI/ML roles — the largest non-remote market in the index — London carries a substantial Data Scientist layer in financial services and tech. JPMorganChase runs active data science teams from London. Spotify’s 11 London AI roles pull on the UK’s unusually deep pool of quantitative and ML talent. For candidates in Europe, London is the primary market by a significant margin.
What changed about Data Scientist roles in 2026?
The core of the job hasn’t changed: take messy data, apply statistical and ML methods, generate predictions or insights that change decisions. What changed is the expected skill floor.
Three years ago, a Data Scientist posting didn’t routinely assume familiarity with transformer architectures or production LLM deployment. Now, at the companies hiring at scale, it does. Capital One’s current data science job descriptions reference LLM customization, RAG pipelines, and agentic workflows alongside the regression and classification work that has always defined the function. Spotify is hiring for audio model development. Reddit needs data scientists who can evaluate large-scale content ranking systems influenced by LLMs.
The result: a bifurcated market. Data scientists with traditional statistical backgrounds but no GenAI exposure are facing more headwinds. Those with strong statistical fundamentals plus applied LLM experience — finetuning, evaluation, inference — are in the highest demand tier of a 287-role category.
Is Data Scientist accessible to early-career candidates?
More accessible than LLM Engineer, less so than ML Engineer on a raw numbers basis.
LLM Engineer has zero junior-level roles in the current index — 0% junior, 63% mid-level, 32% senior. ML Engineer, with its 363-role volume, has at least a small real entry surface. Data Scientist at 287 roles sits between: most postings assume 2-4 years of experience, with a meaningful share requiring 5+.
Company type matters more than raw role count for early-career targeting. Enterprise and financial services Data Scientist teams skew heavily experienced — they’ve run these functions long enough to know exactly what a strong hire looks like, and they’re competing on compensation for proven talent. Earlier-stage companies building data science capabilities for the first time are the better early-career target: they’re hiring for potential alongside experience, and the function itself is being built rather than maintained.
If you’re 1-2 years in and targeting Data Scientist roles, Series A-C companies with actively expanding data needs are the real addressable market — not the Capital Ones and Spotifys where every opening draws hundreds of qualified applicants.
What this means for your job search
Don’t let title inflation narrow your search. The 287 Data Scientist roles sit in a lower-competition environment than “AI Engineer” or “ML Engineer” for candidates with equivalent ML backgrounds. The title has strong search volume — which means employers posting it expect candidates searching for it. If you qualify for ML Engineer roles, you almost certainly qualify for at least a portion of the Data Scientist openings. Apply across both.
Enterprise is the majority of this market. Unlike ML Engineer, where AI-first companies (Scale, Databricks, Mistral) dominate, Data Scientist demand concentrates in financial services, healthcare, and tech platforms. The right positioning frame is production impact on business metrics — not research novelty or frontier model exposure. Quantified outcomes from your past work matter more here than publications or benchmark performance.
The LLM skill gap is closeable. Companies hiring Data Scientists in 2026 increasingly expect GenAI experience that many working data scientists haven’t formally accumulated. That gap is real, but it’s closeable with targeted practice — and closing it before it shows up on your resume puts you in the top tier of a category most candidates are still approaching with 2023 credentials.
Browse current Data Scientist openings alongside Machine Learning Engineer roles, or filter by Remote for distributed options. Index updated every Monday from direct ATS and aggregator sources.
HireIndex tracks 1,789 open AI/ML roles across 748 companies as of May 18, 2026.