How to Find Professors in Data Science With Funding (2026)

"Data science professor" isn't a job title most faculty actually hold, and that's the first thing a search for one needs to account for. Data science PhD training is split across Computer Science, Statistics, and a handful of dedicated data science departments or institutes, sometimes all three at the same university, which means a narrow department-name search misses a lot of the relevant faculty. Funding follows a familiar pattern once you find them, close to computer science's grant-dependent model, but with its own set of named fellowships and its own quirks in how NSF is currently prioritizing the field.

Data science is a research area, not always a department

Some universities do run a dedicated Data Science PhD program or institute. Many more run the equivalent training inside an existing department: Stony Brook's Data Science PhD is jointly administered by its Applied Mathematics and Statistics department and its Computer Science department. Carnegie Mellon's Statistics and Machine Learning PhD sits inside a department explicitly named Statistics & Data Science. NJIT offers a Data Science PhD with a distinct Statistics track housed inside Mathematical Sciences. The practical upshot: if you search only for faculty in a department literally called "Data Science," you'll miss a large share of professors doing exactly the research you're looking for under a Statistics or Computer Science appointment instead. Search by the actual research area, causal inference, high-dimensional statistics, machine learning theory, applied ML in a specific domain, rather than by department label alone.

NSF's National Research Traineeship: funding a cohort, not an individual

NSF's Research Traineeship (NRT) program has funded data-science-themed graduate training cohorts for close to a decade, alongside similarly structured cohorts in AI, quantum information science, and climate resilience. Unlike NSF GRFP, which an individual student applies for directly, an NRT award goes to a university, which then builds a training program and stipend structure that admitted students join as part of a cohort. NSF's broader "Harnessing the Data Revolution" initiative, which NRT's data-science cohorts sit under, remains an active program area rather than one that's been discontinued or folded into something else, with new related funding rounds continuing to be announced. If a program you're looking at mentions an NRT-funded cohort, that's worth asking about specifically, since it can mean a more structured and better-resourced funding package than a standard individual RA position.

Industry fellowships worth knowing by name

Several tech companies run active PhD fellowships aimed specifically at data science and machine learning research. Meta's PhD Fellowship pays $42,000 a year plus tuition for up to two years and is a direct application, not a nomination. Bloomberg's Data Science PhD Fellowship pays $45,000 plus full tuition, but comes with a mandatory 14-week paid summer internship at Bloomberg built into the award. Apple's Scholars in AI/ML program pays full tuition plus up to $40,000 a year with an additional $5,000 travel allowance, but it's nomination-based, meaning your university nominates you rather than you applying directly, so it's worth raising with your advisor once you have one rather than something to plan your initial search around. Amazon launched a new $68 million AI PhD Fellowship Program in October 2025 in partnership with the University of Washington and roughly nine other universities, covering tuition, stipend, and AWS compute credits, a distinctly newer and larger program than any older Amazon-branded fellowship you might see referenced in older content. Treat all of these as competitive, field-specific supplements to a funded position, not as your primary funding plan, and check each program's live page for the current cycle's terms before applying.

Why GRFP and NRT don't help most international applicants

NSF's GRFP eligibility page explicitly restricts applicants to US citizens, US nationals, and permanent residents, and specifically excludes F-1 visa holders and applicants with a green card application still pending. NRT funding works the same way in practice, since it flows through a US-eligible cohort structure. That means, exactly as in computer science, the realistic funding route for an international data science applicant is a research assistantship tied to a specific professor's grant, arranged through direct outreach and faculty matching after admission, not a named fellowship application.

arXiv, and the venues that signal current activity

Checking whether a data-science-adjacent professor is actively working right now works much like it does in computer science: arXiv's stat.ML and cs.LG categories carry a large share of current machine learning and statistical learning research as preprints, often ahead of formal publication. Pair that with a professor's recent acceptances at NeurIPS, ICML, KDD, and JMLR, the field's standard top venues, for a clearer, more current signal than total citation count alone, which tends to stay high for years even after someone's active output has slowed.

2026's tilt toward AI-adjacent funding

Worth knowing if you're weighing where to focus: NSF's GRFP solicitation has explicitly prioritized AI and quantum information science applicants since a 2021 policy change, and by the 2025-26 cycle that emphasis had become pronounced, with industry reporting describing notably higher success rates for AI and quantum applicants than for several other STEM fields. The program's total award numbers were also genuinely volatile through this period, falling sharply in early 2025 during a period of broader NSF budget uncertainty before partially recovering in 2026 once Congress restored funding. If your data science work leans into AI or machine learning methods specifically, that's a real, if moving, tailwind worth mentioning in a US-citizen or permanent-resident GRFP application, though the underlying figures shift enough that it's worth checking NSF's current solicitation directly before relying on any specific number.

A practical search order

• Search by research area and methodology across Computer Science, Statistics, and any dedicated data science department or institute at your target university, rather than by department label alone.
• Cross-check candidates on arXiv's stat.ML and cs.LG listings and their recent NeurIPS, ICML, KDD, or JMLR acceptances to confirm they're actively publishing right now.
• If you're a US citizen, national, or permanent resident, check current NSF GRFP and NRT eligibility and deadlines before assuming either applies to your situation.
• Email the professor directly, referencing their specific recent work, and ask plainly whether they currently have RA funding for a new student, since this is the realistic path for most international applicants regardless of fellowship eligibility.

GradScoutFunding is built for that first step: it searches professors by field and country, confirms they're actively publishing, and drafts a personalized first email grounded in their real recent paper for you to review and send yourself, it never sends anything automatically. The free tier gives 2 credits with no card required, and paid credit packs are one-time purchases that never expire.

Common questions