The document discusses best practices for creating effective resumes in data science, emphasizing the importance of storytelling, relevant experiences, and understanding specific job profiles. It highlights the differences in job application behavior between men and women and stresses the necessity of using keywords and proper formatting. Ultimately, it aims to guide candidates in effectively showcasing their skills and achievements to potential employers.

1. CRAFTING A COMPELLING DATA
SCIENCE RESUME
Jennifer Hay
IT Resume Writer
IT Resume Service
Arushi Prakash
Data Scientist II
Zulily
Women in Data Science Conference
Puget Sound
May 4th, 2020

2. JENNIFER HAY
4
IT Resume writer, 2009 – Present
Specializing in writing resumes and
Linked Profiles for data analysts, data
scientists, data engineers, data
stewards, etc.
Writes data and information
management exams for
eLearningCurve.

3. ARUSHI PRAKASH
Data Scientist @ Zulily, 2019 - Present
Works on recommender systems,
natural language processing, and A/B
testing
Education
PhD, Chemical Engineering, 2018
BS, Chemical Engineering, 2014
5

4. WE WANT TO HELP YOU
1. Best Practices
2. Job Profiles in Data
Science
3. What is the purpose
of storytelling in a
resume?
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TODAY’S AGENDA
Resume
Interviews
Job
Job portals
LinkedIn
Recruiters
Staffing agents
Recruiters
Hiring Managers
Future Co-workers

5. BEST PRACTICES
FOR RESUME WRITING
Including nuggets like “Always keep to a one-
page resume!” and more …
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6. © 2020 Arushi Prakash and Jennifer Hay 8
Format
• Keep it plain and human readable
• Chronological vs. Functional
Writing
• Keep relevant work experiences
• Use keywords from job posts
Writing style
• Use spelling and grammar checkers
• Avoid generic statement

7. UNDERSTAND THE JOB PROFILE
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8. THE FIELD OF DATA
SCIENCE
Diverse
Nascent
Lacks definition
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Computer
Science
Data
Mathematics &
Statistics
Machine
Learning
Engineer
Data
Engineer
Data Analyst
Data Scientist

9. DATA ANALYST ROLES
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10. DATA SCIENTIST ROLES
12

11. MACHINE LEARNING ENGINEER ROLES
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12. DATA ENGINEER ROLES
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13. WHAT DEGREES DO THEY HAVE?
https://medium.com/indeed-engineering/where-do-data-scientists-come-from-
fc526023ace
15

14. WHAT DEGREES DO THEY HAVE?
https://medium.com/indeed-engineering/where-do-data-scientists-come-from-
fc526023ace
16

15. GENDER BIAS IN JOB
DESCRIPTIONS
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16. APPLYING FOR JOBS
Women only apply for jobs if
they meet 100% of the
requirements.
Men apply if they meet 60%.
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https://hbr.org/2014/08/why-women-dont-apply-for-jobs-
unless-theyre-100-qualified

17. THIS ISSUE IS WIDESPREAD
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https://www.ziprecruiter.com/blog/removing-
gendered-keywords-gets-you-more-applicants/

18. THE IMPORTANCE OF
STORYTELLING
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19. Carol’s background
• Conducting economic research and
publishing articles.
• Attending and presenting research findings at
leading conferences.
• Reviewing and critiquing articles written by
other researchers.
• Teaching undergraduate and graduate
economic courses.
Many of my clients focus on the
technology that they delivered. While
this is important, it is also essential to
think about the journey to getting to
those solutions.
I find that it is often the journey that
provides the most compelling
information and brings out a person’s
leadership, management, and technical
expertise.
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FRAMEWORK FOR
STORYTELLING
FRAMEWORK FOR
STORYTELLING

20. • Based on my analytics experience, invited to
speak at WiDS, an organization that inspires
and educates data scientists worldwide.
o Engaged attendees in learning about the
use of data science in economics.
o Received very positive feedback.
o Presented one of my research projects
and performed exercises to illustrate
regression analysis, computational
modelling, and machine learning.
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FRAMEWORK FOR
STORYTELLING
FRAMEWORK FOR
STORYTELLING
Reinforce information.

21. Have you done any of the following:
• Mentored someone through a problem?
• Trained colleagues to use new technologies
or technical practices?
• Given a presentation about your work?
• Advocated for needed improvements?
• These are just few examples.
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FRAMEWORK FOR
STORYTELLING
FRAMEWORK FOR
STORYTELLING

22. FRAMEWORK FOR
STORYTELLING
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Who got
value and
how?
What was
produced?
What were
the
challenges?
What did
you do?
How did
you do it?
What did
you
learn?
value of story elements
low high

23. WHAT DID YOU DO?
• Analytics Lifecycle: data discovery, data
aggregation, planning of the data model,
data model execution, communication of
the results, and operationalization.
• Project Management Lifecycle:
project initiation, planning, execution, and
closure.
• Software Development Lifecycle:
planning, designing, implementing/coding,
testing, deploying, and maintaining.
• Product Development Lifecycle:
concept, user research, feasibility and
proof of concept, architecture and design,
implementation, test and release,
manufacturing, and support.
Step back from your specific work and
think about the overall area in which you
were working. Who is part of your team?
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24. WHAT DID YOU DO?
Created a research process and defined each
step for collaborative teamwork. Taught my
team to:
(1) Ask a meaningful question, (2) Find suitable
data sets, (3) Decide on an appropriate
statistical method, (4) Use data analytics
software to conduct analyses, (5) Answer the
question based on quantitative results, and (6)
Communicate findings both in a written report
and an oral presentation.
Step back from your specific work and
think about the overall area in which you
were working. Who is part of your team?
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25. HOW DID YOU DO IT?
Modernized a bioinformatics environment to
strengthen their primary product and enable
customers to conduct advanced research.
• Converted to a Django framework to enable
rapid development with a large resource
base and to provide a scalable web
framework to meet our growth
expectations.
• Further enhanced the Django application
with AngularJS on the client-side and added a
variety of new features (e.g.“shopping cart
functionality”) to a Drupal based information
system.
Think about the frameworks, tools, methods, and
algorithms you used to achieve the outcome.
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26. WHAT WERE THE
CHALLENGES?
• Short deadlines
• Resource constraints and a lack of in-
house talent
• Significant data quality problems and data
access problems
• A company new to data science with high
expectations for what DS could do
• Technical problems
• Leadership/organizational problems
• A poor data culture and a lack of data
literacy within the company
• A requirement that the data scientist also
be the data engineer
• Data compliance and privacy issues
Think about what made the task difficult to
achieve.
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27. WHAT WERE THE
CHALLENGES?
• Resolved a critical problem in a cloud-based
Human Capital Management (HCM) product
with a soft release scheduled at a major
conference.
• Results: The company filed a patent and my
innovative solution will be used for other
word embedding models.
• Challenges: Model updates required
retraining the whole dataset. Also, it would
violate our customer data policy by merging
customer data and the company’s hiring data.
• Created and tested a new online training
algorithm, which reduced 95% of model
updating time and prevented potential data
security issues in production.
Think about what made the task difficult to
achieve.
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28. WHAT DID YOU LEARN?
• Learned the importance of data quality in
analytic processes during an AI Claim
Lifecycle project to predict the likelihood for
a successful audit of an insurance claim.
o Challenge: Non-standardized claims
created a data integration problem.
o Selected a binary classifier logistic
regression model as the best predictor.
o Defined a new workflow for metadata and
implemented a process upstream to
standardize fields.
• Gained experience in the software side and
the ML side of building production scale AI
applications, using distributed Apache Spark
frameworks, to score very high data
volumes.
This is particularly important for those new
to data science.
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29. WHAT WAS PRODUCED?
• Developed a tool that tracks customer
behaviors and purchasing at a national
grocery chain.
o Designed and created a data pipeline that
included geospatial data for a broader
understanding of the entire region.
o Enabled each business division to analyze
and quickly respond to customer needs
due to the evolving changes in supply
chains.What did you create?Was it a single tool, a
portfolio of services, or an analytic platform?
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If you work in industries that have
been significantly impacted by the
Coronavirus, now is the time to think
about what analytic products can meet
these evolving needs.

30. WHO GOT VALUE AND
HOW?
• Advise undergraduate and graduate
students, mentoring through the process
of determining a career path by channeling
their commitment and passion for
research and analysis.
o Leveraged a student's interest in the
local business environment by
coauthoring a paper on "Startups in Los
Angeles.”
o Resulted in the student getting a
Research Assistant job at the L.A.
County Offices.
o Received the 2019 Outstanding
Graduate Student Research and
Mentorship Award at my college based
on her nomination.
Think about who got value from the success of an
effort or project. Demonstrate and quantify
results here.
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31. RESUME SUMMARIES
A Data Engineer and a Database Engineer with
extensive project management experience as a
Certified PMP, creates major data-driven value
opportunities for the company.
• Turns around troubled projects and
reenergizes the team to regain commitment.
• As an experienced financial analyst, uses data
storytelling techniques to create informative
graphs with comprehensive narratives.
• Works collaboratively throughout the
analytics lifecycle and has particular
expertise in communicating and
operationalizing the results.
In resume summaries it is also important to
think about the issues that we’ve covered.
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32. SUMMING IT ALL UP
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33. LESSONS IN RESUME WRITING
• Follow best practices
• Know which role you are
aiming for
• Tell a coherent story with
what, why, and how
components
• Reach out to people
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