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Stephen Lieberman
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DrivenData Open Competitions

DrivenData (competitions sponsored by mission-driven organisations and agencies)

NontraditionalPrize or challenge

A standing venue where social-sector sponsors post machine learning and data science challenges with cash purses. Entrants compete on a held-out benchmark; there is no proposal, budget or institutional paperwork.

Indicative award
$20k–$50k (varies by call)Purses are set per competition by the sponsor. Those listed as open at the time of checking ranged from $20,000 (Mozilla Data Collective speech recognition) to $50,000 (National Tutoring Observatory), plus a EUR 25,000 medical imaging challenge. There is no standing amount.
Timing
Rolling / openNo specific published date recorded. This funder publishes a cycle rather than a date, and this database does not invent one.New competitions post continuously and each carries its own closing date; competitions listed as open at the time of checking closed in late August and mid-September 2026. Check the listing rather than a fixed cycle.
Who can apply
Master's / undergraduate, Doctoral candidate, Postdoctoral, Early-career faculty, Any faculty rank, Practitioner
Eligibility
Open to individual data scientists and research teams worldwide, including students and doctoral researchers; entry is by account registration and a submitted model, not by institutional application. Prize eligibility can be restricted by sponsor-specific rules and export controls, so each competition's own rules page governs. Sponsors, not DrivenData, set the purse.
SSWR clusters
Topics
machine learning · predictive modeling · speech recognition · education data · health imaging
Apply at
https://www.drivendata.org/competitions/
This entry
Curated by the working group. Written 2026-08-07. Funder link last confirmed to resolve on 2026-09-25.

Amounts and dates on this page are indicative and are maintained by people rather than scraped. Funders change ceilings, windows and eligibility without notice; confirm all details at the source above before applying.

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