Loading organizations...
§ Private Profile · 4265 San Felipe St Suite 1025 Houston, Texas 77027, USA
SciOps platform automating scientific data management & analysis for life sciences research, focused on reproducibility & AI.
DataJoint is a developer of a scientific operations platform and computational database that structures, automates, and scales data management and analysis for complex life sciences research. Operating with a team of approximately seven key personnel, the enterprise provides subscription-based software that replaces fragmented data handling with reproducible, automated workflows. The company has achieved significant adoption across the academic and pharmaceutical sectors, serving hundreds of research laboratories worldwide and generating more than $500,000 in annual recurring revenue as of mid-2024. In August 2025, the organization closed a $4.9 million seed funding round to further develop its artificial intelligence capabilities and support collaborative projects involving prominent institutions like Baylor, Stanford, MIT, and the NIH. Originally established as Vathes LLC in 2016 before reincorporating under its current name in 2024, DataJoint was founded by Dimitri Yatsenko and Montgomery Kosma.
DataJoint has raised $57.8M across 3 funding rounds.
DataJoint has raised $57.8M in total across 3 funding rounds.
DataJoint has raised $57.8M across 3 funding rounds. Most recently, it raised $49.0M Seed in September 2025.
| Date | Round | Lead Investors | Other Investors | Status |
|---|---|---|---|---|
| Sep 30, 2025 | $49M Seed | Capital Factory, Inoca Capital Partners, Nina Capital | — | Announced |
| Sep 1, 2025 | $5M Seed | — | Connect Ventures, Nina Capital, Sarah Friar | Announced |
| Jun 30, 2020 | $3.8M Grant | National Institutes OF Health | — | Announced |
DataJoint has raised $57.8M in total across 3 funding rounds.
DataJoint's investors include Capital Factory, Inoca Capital Partners, Nina Capital, Connect Ventures, Sarah Friar, National Institutes of Health.
DataJoint is a technology company building a SciOps platform—a computational database that structures, automates, and scales scientific data management, computation, and analysis for life sciences research. It serves academic labs, research institutions, and pharmaceutical organizations in fields like neuroscience, oncology, systems biology, and AI, solving the problem of ad hoc, fragmented data handling that leads to errors, irreproducibility, and wasted time[1][3][4][6]. The platform replaces manual processes with automated workflows, cutting 80-90% of data cleaning time, ensuring reproducibility, and enabling AI/ML integration, as used by over 100 labs at institutions like UCSF, Harvard, Johns Hopkins, and University College London[2][3][6]. With $4.9 million in seed funding raised recently, DataJoint shows strong growth momentum, expanding its SaaS platform into commercial life sciences and pharma sectors in the U.S. and Europe[6].
DataJoint emerged from founder Dimitri Yatsenko's experiences in neuroscience and tech startups. After working at Ripple Neuro and earning a PhD at Baylor College of Medicine, Yatsenko co-founded nView Medical, an AI and medical imaging company, where he identified critical gaps in research data handling and AI model development[6]. This insight led him to launch DataJoint, initially as an open-source framework for shared scientific databases and computational pipelines, before evolving into a full commercial platform focused on neuroscience and AI[5][6]. Key early traction came from adoption in top academic labs, validating its role in streamlining complex workflows; today, under CEO Jim Olson, it supports premier institutions while scaling commercially[3][6].
DataJoint stands out in scientific data management through its integrated Computational Database, which unifies relational databases, object storage, source code management, and workflow orchestration for end-to-end reproducibility[4].
DataJoint rides the wave of AI-driven life sciences and reproducible research mandates, where exploding multi-omics data volumes demand scalable infrastructure amid NIH policies requiring data sharing and integrity[3][5]. Timing is ideal as pharma and academia grapple with AI/ML workflows on unstructured data—DataJoint's platform harmonizes this, accelerating discoveries in neuroscience (e.g., multi-animal behavior tracking) and oncology[2][6]. Market forces like rising compute costs and collaboration needs favor it, influencing the ecosystem by open-sourcing elements (via INCF partnership) and enabling cross-lab data sharing, bridging academic innovation to commercial drug development[4][5][6].
DataJoint is poised to dominate SciOps as AI transforms life sciences, with plans to grow its team, enhance SaaS features, and penetrate pharma markets using its $4.9M seed from Nina Capital, Inoca Capital, and Capital Factory[6][7]. Trends like multi-modal AI, long-term biomedical projects, and regulatory pressures on reproducibility will propel it, potentially evolving into a standard for global research consortia. As labs multiply impact through structured science, DataJoint's platform—replacing fragmented tools with automated rigor—positions it to streamline the path from raw data to reliable findings faster than ever[1][3].