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NIH Seeks Inaugural Chief Data Strategist

Posted on by Dr. Francis Collins

The NIH is seeking a visionary technology leader to serve as our inaugural Chief Data Strategist. I anticipate this will be someone with industry experience who can deliver next generation technology platforms to harmonize our massive, complex data sets and build a 21st century workforce. Several colleagues and I filmed a video to share more about this position and how it can change the future of computing, while accelerating life-saving research breakthroughs for decades to come. Learn more and please consider applying. Credit: NIH


  • Mike Hasan says:

    Its a Good News

  • Jason Williams says:

    Unfortunately, no amount of vision will solve the major challenges NIH will face in doing this correctly. NIH has every resource and talent to pull this off. What NIH does not yet have is a management culture that can separate poison politics and subjectivity from complex initiatives like these. This position and effort is of existential necessity if NIH wants to remain relevant to science (outmoded data strategy will be malpractice in the next decade of research – no NIH budget increase will compensate for this). This position needs to be *empowered* by expert and community recommendations that NIH leadership is accountable to (not the other way around). Otherwise, whomever accepts this position will soon see their vision blinded by unresponsive and siloed viewpoints, gossamer handholds on technology, and lead-heavy bureaucracy.

  • Melissa Haendel says:

    I agree with Jason ;-). However one must start somewhere. I think it is fantastic that NIH is hiring someone to play this role. What needs to happen is not only empowerment and guidance from and in collaboration with the community, but also an overarching strategic vision for how intramural and extramural activities across NIH ICs can work together synergistically instead of in competing silos. Further, there needs to be a deep understanding and commitment to of all aspects of the data life cycle. There should be just as much importance placed on data curation and data standards and tools to support these efforts as on sophisticated computer science technologies such as machine learning. All of these technologies benefit enormously from – and depend on – quality, clean, well structured and well maintained data. I fear that an undue focus on the current technical innovations will overpower the potentially less exciting but critically foundational data stewardship and socio-technical engineering that are required for this role to be maximally impactful.

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