Academic Affiliate Meet-Ups Planned

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The first meet up for academic affiliates has been scheduled for Wednesday, October 10th, 2018  from 12 – 1 pm ET.   This is a first in a series of virtual meet-ups for the NISS Academic Affiliates and this time the target audience is graduate students. 

The focus of this first program is “Landing Your First Job as a Statistician”.  Speakers/panelists include members from academia, industry and government, either senior individuals who have either been regularly involved in hiring statisticians, or were recently hired as a statistician.  Each panelist will talk about their hiring process, the qualities and skills they look for in candidates, and the do’s and don’ts during the interview.  If you are now, or will soon be on the job market, this conversation is one that you will want to join!

Confirmed Speakers for this meet-up are Tim Hesterberg (Google), David Hunter (Penn State University), Esra Kurum (University California, Riverside), and Tommy Wright (US Census Bureau).

The following topics are being planned for future meet-up programs for academic affiliates.

Early December 2018:  “Strategies for Career Advancement in Academia”.  This program will focus on guidance on career development in the academic setting including what you might want to consider doing to prepare for leadership opportunities.

Early February 2019:  “The Pros and Cons of Getting Degrees in Statistics, Computer Science, or Data Science”. This program will focus on how the different curricula differ, how employers of data scientists/analysts view candidates from the different majors and the skills they are looking for.  The panel will include both employers and directors of data science training programs.

Some time in April 2019 “Effective Collaboration”. This program will focus on how to develop successful collaborations in academia.

You will find additional information about these and all the other events that NISS hosts or sponsors on the NISS Events page

Sunday, August 12, 2018 by Glenn Johnson