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HiveMined is a job board with a deliberately plainspoken identity, and its data science section reflects that spirit. Listings here focus on academic and industry roles in data science, statistics, machine learning, and artificial intelligence, ranging from postdoctoral and research positions to lecturer, reader, and tenure-track faculty appointments. The emphasis is on clarity rather than volume: each post carries the institution, the discipline, and the level of the opportunity so that candidates can quickly judge relevance before opening the full listing.
The career academy format sits alongside more conventional postings on the board. Rather than advertising a single vacancy, an academy listing describes an ongoing programme of study, mentoring, and practical exposure for people building a foundation in data science. This kind of entry suits candidates who want structured instruction, project work, and guidance toward their first professional step instead of an immediate appointment, and it appears among shared postings contributed by educational institutions alongside universities and research organisations.
Browsing the data science category on HiveMined surfaces roles across very different settings, including universities, medical and health schools, engineering faculties, and public research bodies. Titles seen in this space include assistant professor, associate professor, senior scientist, biostatistician, statistician, data engineer, decision analyst, and research fellow. Many are advertised with a specific track, whether teaching-focused, tenure-track, or fixed-term, giving applicants a clearer picture of the expectations attached to the post before they invest time in a full application.
The board treats each listing as a shared item rather than a sealed advertisement, which keeps the tone practical and community-oriented. Posting dates appear plainly beneath the entry, and the surrounding design stays restrained, with generous whitespace and a straightforward typographic hierarchy that puts the job title and institution in charge. The result is an experience designed for scanning: readers move through discipline and location, spot the level of the role, and open only the listings that genuinely match their background and career stage.
For anyone tracking opportunities in data science and adjacent fields, HiveMined offers a broad, no-nonsense catalogue that mixes academic posts with professional and industry roles. The data science and machine learning sections sit within a wider board that also covers natural language processing, biomedical analytics, health data, and decision support work. Because listings are shared and dated, the picture stays current without the noise of promotional material, making it easier to identify openings that fit a specific skill set, contract type, or preferred location.