The Harvard Business Review predicted a while back that a “data scientist” would be the “it” job of the 21st century. However, a lesser known side of data scientists is that their job can be less than glamorous. While the data scientist is expected to be a “unicorn” and do it all, there are major issues and roadblocks that arise when tackling any new data frontier.
Last month, our own predictive analytics scientist, Virginia Long, sat down with Katherine Noyes of CIO for her article: “Why being a data scientist ‘feels like being a magician.” Virginia shared what it’s really like being a data scientist, elaborating on her typical day, her favorite aspect of working with data, common roadblocks she encounters – and dispelled some common myths about her profession.
In the article, Virginia discussed her day-to-day responsibilities:
- Creating educational materials to explain how various data science techniques work
- Painting the big picture for companies and clients as to what their data means
- Managing the expectation of the data scientist, who is often expected to be a “unicorn”
Interested in learning more about the role and responsibilities of data scientists? Check out the full-piece on CIO here. If you’d like to learn how you can utilize data analytics to solve your most pressing issues, check out our solutions here.
Get our take on industry trends
How to help employer groups plan in a time of uncertainty
Employers and their sponsored health plans are thinking about next year’s benefit designs with a significant challenge not seen before: the effect of the coronavirus pandemic. There are important considerations to take into account before making any decisions about new or existing coverage. Becky Niehus, a director of Product Consulting at MedeAnalytics, explores these new issues and what employers can do to ensure employees are “covered.”
Read on...Healthcare’s return to “normal” after COVID-19: Is it possible?
As providers determine how to get patients to return to facilities for routine disease management and preventive screenings, opportunities are ripe for the application of analytics to triage at the right time to the right setting. Data related to COVID-19 will continue to flow rapidly, but there are possibly more questions than answers now about a return to “normal.”
Read on...Avoid COVID-19 modeling pitfalls by eliminating bias, using good data
COVID-19 models are being used every day to predict the course and short- and long-term impacts of the pandemic. And we’ll be using these COVID-19 models for months to come.
Read on...Population Health Amid the Coronavirus Outbreak
In speaking with many colleagues throughout the provider and payer healthcare community, I’ve found an overwhelming sense of helplessness to the outbreak’s onslaught. This is exacerbated by the constant evolution of reported underlying medical conditions that indicate a higher risk of hospitalization or mortality for a coronavirus patient.
Read on...