The no-nonsense job board for data, statistics & AI

Listings at a glance

HiveMined is a job board built for data, statistics and artificial intelligence work, with a particular focus on the academic and research side of those fields. Instead of scraping and reposting advertisements from a careers portal, it gathers listings shared directly by the people who work in the areas they post about. That sourcing model is the whole point: the contributors are statisticians, data scientists, researchers and teaching staff who know their own field well enough to spot a genuinely interesting opening.

The categories cover the breadth of a research career cycle. Faculty and tenured professor posts sit alongside senior scientist and reader positions, associate and assistant professor roles, and entry points such as postdoctoral fellowships and PhD projects. There are also graduate and undergraduate teaching positions, career academies and fellowships for early-career researchers, plus more specialised listings in biostatistics, health and medical sciences, natural language processing, and machine learning.

Every listing names the institution, the role and the contributor who posted it, so a reader can judge the source before deciding whether to apply. That transparency matters in research recruitment, where aggregator sites often blend scraped copies of the same vacancy into a maze of near-identical entries. On HiveMined the attribution is visible, and the surrounding job details give enough context about the department, grade and duties to know whether the fit is worth pursuing.

Because the board is shared rather than sold, the listings skew toward the kinds of positions that researchers themselves watch for: fixed-term research posts, doctoral and postdoctoral opportunities, and teaching-focused roles at universities and institutes. Community contributors also tend to surface openings early and across a wider spread of institutions than a single institution's own recruitment page would reveal, which is useful when the internal network is narrow.

Institutions represented on the board span universities, medical colleges, research institutes and national laboratories across several countries, and the keyword trail reflects that geographic spread. Names associated with computing, statistics, mathematics, engineering, biomedical analytics, decision science and law all appear, along with humanities-adjacent areas such as language and networks, showing how far the data and statistics job market now reaches beyond departments with those exact titles.

Searching the board means filtering by discipline, career stage or location, and the results read like a well-kept academic contacts list rather than an auction of promoted adverts. Roles tagged for teaching, research support, statistical analysis or systems and decision work can be narrowed down quickly, and the mix of permanent, tenure-track, fixed-term and project-funded posts makes it straightforward to compare contract length before spending time on an application.

A separate section introduces the people behind the listings, which turns the board into something closer to a professional network than a marketplace. Seeing who shares what, and in which field, gives readers a sense of the communities that populate these roles: departmental groups working on clinical trials, bioinformatics, causal inference, forecasting, and decision support, often crossing institutional boundaries in ways that a formal careers database would flatten out.

Application routes are kept deliberately plain. Each post provides the role details, the institution and the contributor's handle, so readers know exactly where the information came from and who to follow for more openings in the same area. There is no intermediary between the reader and the hiring institution beyond the person who chose to share the vacancy, which keeps the process short for candidates and light on administration.

The design language suits the audience. A deep navy and indigo base with a vivid purple-blue accent, generous white space, pill-shaped buttons and circular badge motifs reads as polished but restrained, closer to a modern professional service than to a loud recruitment marketplace. Headlines set in a clean grotesque-style sans-serif keep long academic titles legible, while thin dividers between list items help dense listings stay scannable.

HiveMined's premise is simple: the people doing the work are the best ones to share the openings. By crediting contributors, trimming the noise that crowds generic job boards and staying focused on data, statistics and AI, it offers a quieter way for researchers, scientists and teaching staff to find their next position, whether that is a first PhD project, a postdoctoral fellowship, a lectureship or a full faculty chair somewhere along the tenure track.