2019 Women in Analytics Conference Katie Sasso-Schafer, Ph.D. and Kelly Denney Ph.D. womeninanalytics.com

1. From Academia to Industry:
Taking the Plunge
Katie Sasso-Schafer, Ph.D.
(OSU - Experimental Psychology)
Data Scientist,
Columbus Collaboratory
Kelly Denney, Ph.D.
(OSU - Astronomy)
Director of Data Science Strategy,
Radiology Partners, LLC.

2. My path to “Industry”

4. CV → Resume
“Research Experience (CV)” “Relevant Analytic Experience”
Graduate Research Associate
● Employ hierarchical linear modeling techniques,
factor analysis, and mediation/moderation in
order to critically assess predictors of outcome
and methods focused questions in depression
treatment
● Study coordinator for large scale multi-measure
treatment study with responsibilities including
oversight of recruitment, patient randomization,
screening, and protocol adherence
● Write code in statistical packages to integrate
and analyze clinical trial data (2,000+
observations)
● Critically evaluate research and provide written
reviews and recommendations for action to
journal editors

5. CV → Resume
“Research Experience (CV)” “Relevant Analytic Experience” cont.
Undergraduate Research Associate
● Learned SAS programming and applied skills to write code, analyze data, and interpret
findings
● Learned basics of R statistical package and assisted with experimental design
implementation, and data cleaning
Consider a “Data Science Skills” section to abstract your subject matter
● Focus on the statistical methods you’ve mastered
○ Multi-level modeling, factor analysis, structural equation modeling
● Focus on other routine data manipulation, visualization, and munging techniques you
typically use.
○ Cleaning, outlier evaluation, imputation of missing data, assessing model fit
● Mention programming tools you use (i.e., R, SAS, Python, MATLAB)
● Use your network. Find someone in industry to review your materials and validate your
“translation”

6. CV → Resume
“Publications and Presentations” A bullet point
● Publish empirical findings in multiple peer-
reviewed journal outlets and speak at
international conferences

7. CV → Resume
The cutting room floor

8. LinkedIn
Data Science “Skills” Section
● Create SQL queries to combine data resources and write code in SAS, R, LISREL, G*power, and SPSS. Beginner’s proficiency
in HTML, CSS, and JavaScript
● Generate and analytically test predictions about consumer behaviors and perceptions, synthesize results into insights, and
translate insights into actionable solutions
● Employ a variety of research design and analytical methods in order to answer research questions in the most efficient and
cost-effective manner available.
● Integrate analytic skill set with expertise in personality psychology in order to predict consumer behavior and risk

10. Navigating Job Listings Aka Intimidation 101
• Requirements are a Wish
List – Go for it with ~60%
alignment (Men statistically
have a lower “apply” threshold
than women…)
• Tailorthe “description” of
your experience to highlight fit.
• Hand your resume to a
person and have a
conversation with a direct
point of contact.
• Read between the lines: what
soft skills are implied?

11. Soft Skills:
creativity, hacker mindset, inquisitive, collaborative,
outside-the-box thinker, innovation, communication,
organization, problem solver, critical thinker, time
management, curiosity, charisma, business acumen,
presentation savvy, alternative perspective, people
skills, leadership, PhD = professional learner,
experiment design, work ethic, adaptability, grit,
perseverance, story telling, tenacity, motivated, self-
starter, growth mindset, takes initiative....
As important as technical, or Hard Skills

12. Some Tips..
● Time Tracking + Mood (Activity Log!)
● Pros and Cons
● Sunk Cost Effect
● Be mindful of others time
● Focus resume skills on general techniques you applied to data, e.g.,
○ I didn’t measure masses of extragalactic supermassive black holes using
reverberation mapping techniques.
○ I applied a time series analysis to data that I sampled, cleansed, and analyzed (or
modeled) with statistical methods in order to extract meaningful trends.
● Don’t assume anyone has any idea what a PhD with your background does,
or is capable of doing
● Don’t worry about looking for that “one” job
● Consider a professional recruiter/head hunter

13. Q&A