CCD Series: Along Your Career Path in Statistics and Data Science
Elizabeth Mannshardt, ASA CCD Vice-Chair 2021
UPCOMING - Career Next-Steps and Promotions - Friday June 11 2021 at 1pm Eastern. Register
Early Data Science Careers and the Job Search Process Recording
In 2021 ASA CCD launched its first Webinar Series: Along Your Career Path in Statistics and Data Science. The first offering, Early data science careers and the job search process, was held in April with over 65 participants. CCD hosted a panel of industry, academic, and government representatives talking about possible career paths in Statistics and Data Science, including tips and insights on the job search and application process. Panelists included Nancy Murray, Statistician at CDC and Biostatistics PhD Candidate at Emory University; Won Chang, Assistant professor at University of Cincinnati; and Dr. Diane (Di) Michelson, JMP. The panel was moderated by Ruth Hummel and hosted by Donna LaLonde with ASA's CCD.
Each panelist expressed what they appreciate about their type of work and why they chose that path. Di wanted to be in industry to do something different every day and enjoys the variety of work and projects. Nancy appreciates the work-lifestyle balance with clear-cut distinction between work and off time, and enjoys a mix of curiosity, coding, communication in her day-to-day work. Won appreciates the flexible schedule and being able to figure out one’s own best direction and plan to navigate issues. Each commented that there are many different types of careers in data science, and Statisticians and Data Scientists learn methods that can be applied to many different areas. A common theme among panelists was the importance of communication in their work. An important point is that we often need to make career decisions based on family needs and choices.
The panelists advice on finding jobs included
- “Take advantage of opportunities outside of your current position”;
- “Participate in summer programs, research opportunities, and ASA”;
- “Importance of job search process at conferences and postings via statistics websites and list-serves”.
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Put stuff on website, when you meet people at conference you can send them to your website. Note ASA CCD's Technology Adaption for Online Presence initiative: https://community.amstat.org/ccd/portfoliotechnology
- Conference on Statistical Practice; Consortium on Applied Statistics; Women in Data Science
- Branding and Networking are key
- talk to professors; join student chapters; join sections of ASA. Even ones that may not be your title/area – can learn so much from discussions
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Academics: Go to websites fall before your graduation. Make big list and just keep applying. No upper limit for number of applications submitted.
All panelists agreed on the importance of networking, with Di commenting: “Who you know can get you in the door – what you know gets you through the door.” Branding is very important.
Participant feedback from the Early Career session included
- "Thank you for the insights re: branding, very useful to know”;
- “great advice and helpful link”;
- “Thank you! Very informative”;
- “Very useful and interesting information”.
The panelists each offered advice and described their day-to-day life:
Won (Academics)
discussed the Trinity of academic life: Research, Teaching, Service (administrative work and committees)
Advice during PhD: One paper published; two in the pipeline - the job process was a bit different than he expected. Do have to make sure you complete a certain number of papers per year
Day in the life: Work time can be flexible; A lot of research meetings;vTeaching – lighter load in summer. You figure out your best direction and make a plan to navigate issues
Nancy (Government)
Current job: curiosity, coding, communication; Lifestyle: clear-cut distinction between day time work and off time
Day to day: translational research – talking to data analysts, research statisticians – what problems are they trying to solve; both current and in the future. Not as much time to sit down and do the math – lots of meetings and collaborations
Advice during school: take advantage of other opportunities: Summer programs; research opportunities; ASA;
Job search: Started her job search process at ENAR – career center, nominal fee, submit resume; Applied to positions that she saw via list-serves; Usajobs.gov
Received a job offer though went another path. Importance of considering personal and family needs.
Diane (JMP/SAS)
Day-to-day: Di wanted to be in industry to do something different every day – Not into research; If your goal is pure research, that can be hard to find in industry
A lot of collaboration; being able to communicate about very difficult concepts
35 hrs/week - Life style – turn off in the afternoon; nice balance.
Advice in School: What do you need to study? Design and analysis of experiments; Process control; Reliability analysis; All kinds of models – time series, forecasting, multi-variate analysis, functional data analysis (curve), predictive modeling & machine learning, Text analysis - We learn methods that can be applied to many different areas
Participant questions:
What education is needed
How much do you emphasize the need for a Phd over the needs for learning skills without a PhD from your 30 year experience as a statistician? Is PhD an absolute need to progress in the industry?
What would your recommendations be - to branch into statistics besides a PhD (any courses, certifications etc)?
How do you decide whether to go the post-doc route or apply to an academic position?
Diane answer: A PhD is not a requirement to work in industry - a master's degree is often enough. (I didn't know what I wanted to do when I got my master's in math) Having a PhD is a benefit for my employer. However, I use the skills I learned in working on my PhD (collaboration, researching topics, teaching) daily.
Many companies are only interested in hiring new grads. They want to train you themselves.
Won: you do need a PhD to be a professor, though you can often be a lecturer or teacher with a Masters.
If you do a postdoc you may be able to find a more competitive position; but you can also lose a bit of time. So it depends on the situation and your goals. Postdoc mentor is very important, more important than the fancy school. Some advisors are much better than others; just because they are good PhD advisors does not mean that they are good postdoc advisors
Nancy: people often come into government with Bachelors or Masters. PhD often not required but Masters sometimes are; masters’ plus experience etc. Specific to criteria on job posting. Look through postings on usajobs.gov. My position – PhD not required but I have used many of things that I have learned during my PhD
JOIN ASA CCD's next in Your Career Path - Career Next-Steps and Promotions - Friday June 11 2021 at 1pm Eastern. Register
Panelists: Early data science careers and the job search process. Friday, April 30 at 1pm Eastern.
Nancy Murray, Statistician at CDC and Biostatistics PhD Candidate at Emory University
Nancy Murray holds a Bachelor of Science in statistics from the University of Tennessee, Knoxville and a Master of Science in biostatistics from Emory University. She is currently pursuing her doctoral degree in biostatistics from Emory University. She joined CDC in Fall 2020 as a Statistician in the Influenza Division of the National Center for Immunization and Respiratory Diseases.
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Won Chang, Assistant professor at University of Cincinnati
Dr. Won Chang received his PhD in 2014 from the Department of Statistics at Penn State. After his graduation he worked as a Postdoctoral Scholar at the University of Chicago for two years. In 2016 he joined the Department of Mathematical Sciences at the University of Cincinnati as an Assistant Professor. Dr. Chang will speak and answer questions about job-seeking in academia.
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Dr. Di Michelson, JMP
Dr. Diane K. Michelson develops courses, teaches classes, and mentors students in statistical methods using JMP software from SAS. Learners come from industries including pharmaceutical development and manufacturing, chemical processes and refineries, semiconductor manufacturing, and others. Prior to her role at SAS Institute, Di spent over 20 years as an industrial statistician in the semiconductor industry, both at an IC manufacturer and at Sematech, the industry’s research consortium. Di holds a Ph.D. in Statistics from Texas A&M University. She has served ASA as a PStat committee member, various officer positions in the Quality and Productivity section, various officer positions in the Austin chapter, and chair of the Hahn award committee.
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