Episode 96: Data Science Training and The Power of Education with Merav Yuravlivker

Show Notes(02:18) Merav talked about her undergraduate experience at McGill University studying Psychology and Sociology.(04:33) Merav discussed important attributes of an exceptional teacher given her two years teaching elementary special education in NYC public schools through the Teach For America program.(08:19) Merav commented on her time working at the International Baccalaureate Organization and working as a Kaplan GRE instructor.(10:57) Merav shared the backstory behind the founding of Data Society, a predictive analytics training and consulting company (co-founded with Dmitri Adler and John Nader).(14:15) Merav reflected on her journey into programming.(17:16) Merav explained why data science training should be industry-tailored for maximum success.(20:57) Merav talked about how Data Society creates and evaluates its training curriculum.(23:59) Merav provided an example of how Data Society provides customized AI solutions to inform decisions, automate time-consuming manual processes, and solve complex data challenges for its clients.(27:38) Merav brought up challenges that hinder the adoption of data science in the government sector.(29:49) Merav unpacked the six different steps for organizations to start moving up the data analytics maturity model.(33:07) Merav dissected meldR, Data Society’s internal product built for Learning and Development teams in healthcare.(36:24) Merav reflected on bootstrapping Data Society in the early days (look at this 2016 Kickstarter campaign).(39:48) Merav discussed the shift from a B2C to a B2B model for Data Society and scoring partnerships with Fortune 500 companies and federal agencies.(42:47) Merav shared valuable hiring lessons to attract the right people who are excited about the mission of Data Society.(45:22) Merav shared her experience shaping the remote work culture.(49:05) Merav touched on initiatives at Data Society to bring more goodness to the world.(50:28) Merav provided different ways to engage more women in data science (via the Women Data Scientists DC Meetup and DCFemTech).(53:17) Merav predicted the evolution of education in the next 3 to 5 years.(55:29) Closing segment.Merav’s Contact InfoLinkedInTwitterData Society’s ResourcesWebsiteTwitterLinkedInMentioned ContentArticles“Is Your Enterprise Data-Driven?” (May 2021)“Why Data Science Training Should Be Industry-Tailored for Maximum Success” (August 2021)“Female Founders: Merav Yuravlivker of Data Society On The Five Things You Need To Thrive and Succeed as a Woman Founder” (Sep 2021)PeopleDJ Patil (The first Chief Data Scientist of the US)Hilary Mason (Co-Founder of Hidden Door)Avriel Epps-Darling (Ph.D. candidate, Ford fellow, and Presidential Scholar at Harvard University)BookWeapons of Math Destruction (by Cathy O’Neil)Notes

My conversation with Merav was recorded back in December 2021. Since then, many things have happened at Data Society. I’d recommend:

Reading Merav’s articles on Forbes about creating a culture of data sharing, assessing data literacy, and communication in the learning process.Reading Data Society’s white papers about data science in research and data science in healthcare.Checking out the Camelsback product for risk assessment in financial services.Trying out the Data DNA assessment tool for organizations’ data maturity.

Finally, Merav was also just recognized as one of the DC region's 40 Under 40. The awards are given annually to recognize the outstanding achievements of young leaders in the Washington, DC, area who lead the community forward through hard work, philanthropy, and community engagement.

About the show

Datacast features long-form, in-depth conversations with practitioners and researchers in the data community to walk through their professional journeys and unpack the lessons learned along the way. I invite guests coming from a wide range of career paths — from scientists and analysts to founders and investors — to analyze the case for using data in the real world and extract their mental models (“the WHY and the HOW”) behind their pursuits. Hopefully, these conversations can serve as valuable tools for early-stage data professionals as they navigate their own careers in the exciting data universe.

Datacast is produced and edited by James Le. For inquiries about sponsoring the podcast, email [email protected].

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