A data-heavy research arm of the Centers for Disease Control and Prevention wants the entire organization to get better at data collection and analysis, whether employees’ degrees are in biological sciences or data science.
The CDC’s National Center for Injury Prevention and Control, or NCIPC, includes a Data Analytics Branch charged with conducting “methodologic research and data analysis, addresses existing data issues and new data challenges—big, complex, non-traditional data—and develops and disseminates data, tools, and applications,” according to a request for quotes posted to SAM.gov. “It offers expertise in statistics, economic analysis, programming and data science to NCIPC and other partners. It also provides national, state and county-level data on injury morbidity and mortality.”
The Data Analytics Branch, or DAB, added a data science team in January 2020, consisting of “statisticians, computer scientists, programmers, and health scientists who use and promote data science for injury prevention and control,” the RFQ states.
The team is made up of credentialed data scientists. But in an ever-evolving field, regular training is a must.
“Successful implementation of data science goals in the Injury Center will depend on many factors including successfully strengthening the data science workforce and advancing information technology infrastructure,” the solicitation states.
But to be truly successful, the agency wants to improve data science literacy at every level.
To improve data science work at NCIPC overall, officials want to “improve general knowledge and awareness of staff scientists” who may or may not have a data science background, as well as providing opportunities for “upskilling of the data science team through advanced trainings and consultation.”
The agencywide training will include a two-hour monthly seminar series, with topics like: intro to data science, data wrangling, machine learning theory, time series analysis and social media analysis, among others.
For the data science team, the vendor will be expected to host 12-16 half-day trainings throughout the year. Those seminars will focus on advanced techniques, such as: intermediate neural networks and deep learning, Bayesian inference, Markov Modeling approaches and advanced R and Python coding, among others.
Along with the trainings, the contractor will be expected to provide “expert data science consultation”—including at least one doctorate-level data scientist—for specific research efforts. The expert must have at least three years of experience, “with emphasis on machine learning and natural language processing,” the RFQ states.
The contract will run for one year, with all services and trainings will be provided virtually over Zoom or Microsoft Teams.
Responses to the RFQ are due by 5 p.m. Aug. 26.