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Tips for Effective
Data Science in
the Enterprise
Lisa Cohen
2
Session goals
• Demystify Data Science Career Paths
• Discuss best practices to tackle a Data Science Project
• Gain Tips & Tricks for DS scenarios in the enterprise
2000
Applied Math
Bachelor &
Masters
degrees
Quantitative
thinking,
Applied sciences
Cum laude
2004
VS Languages & IDE
Technical Feature PM
Building software,
understanding customers,
leading cross-functional
feature team
Incorporating SQL & data
access into .NET
programming languages
Shipped VS 2005, 2008, 2010
Reached ~9M customers
3 patents for new designs
2012
Sr Mgr –
VS Telemetry
Product analytics:
working with perf,
compatibility, planning,
privacy, compliance
Led DevDiv business
reviews, Advanced VS
telemetry, Delivered VS
active use clustering,
Launched “Send A
Smile” feedback
2015
Principal Data
Scientist Mgr
Using data to help
customers succeed &
grow on Azure. Driving
DS best practices,
Cross-MS partnerships
Led cross-functional
team. Evolved DS for
credit offers, partners,
Direct customers,
Support.
2008
Sr Community PM - VS
Divisional strategy,
industry trends, Central
org & systems, Cross
group partners, Exec
comms, CSAT & customer/
partner communities
Led DevDiv blogs (3M+
views/mo), Presented
keynotes & sessions at
50+ conferences
YR
Role
Experience&
Learnings
Achievements
Nurturing customers, growing the business, connecting sources and advancing the DS craft
Students &
Developers
Student offer, VSE
Direct /
Unmanaged
Individuals, SMC, Sign-Up
Field & EA
Enable Sales
Cross Cloud, Support, Retention, Service Usage, Fraud, Payments
Data Science, Machine Learning, ML Ops, Experimentation, Data Vis, PM
Partners &
Startups
ISVs, CSP, PAL, DPOR, MfS
Marketplace App Source Partner Center Customer Portal
Azure Portal Advisor Cost Management Azure.com Docs Learn
PORTALSAUDIENCES
FUNDA-
MENTALS
CAP-
ABILITY
6
Session goals
• Demystify Data Science Career Paths
• Discuss best practices to tackle a Data Science Project
• Gain Tips & Tricks for DS scenarios in the enterprise
Demystifying Career Paths
 Roles: What excites you about Data Science?
Data Scientist
Analytics & Inference
• Statistical analysis & experiments
Machine Learning Scientist/Engineer
Production Models
• Develop predictive models, MLOps
Data Engineer
Data Platform & Pipelines
• Build the data platform
Program Manager
Planning & Stakeholder Engagement
• Manage the data science process
Tip #1: Follow your passion
The Data Science Venn Diagram
 Technical
 Analytical Problem Solving
 Statistics
 Querying
 R, Python, SQL, Kusto
 Big Data
 Modeling
 Data Visualization
Technical
Soft
Skills
Domain
 Domain
 Business context
 Data sets
 Soft Skills
 Communication
 Organization
 Cross-Group Collaboration
 Teamwork
Conway’s Venn Diagram
Tip #2: Chart your path
Data Science Organizations
 What kind of environment do you want to be in?
CentralizedEmbedded
A core data science org
provides services to
business or functional
teams across the
company as a center of
excellence
Individual data science
teams are spread
throughout the company,
reporting to and serving
specific business or
functional teams
Tip #3: Find a DS community Tip #4: Connect with Stakeholders
10
Project Intake Tips
Kicking off a model, experiment or analytics project
 What new capability will this enable?
 What decision/action will you take?
 What’s the expected impact?
Planning Process
Project Intake
Questions
Prioritization &
Scalable Solutions
Tip #5: Focus on what matters (Prioritize with stakeholders, ask questions, socialize results)
Data Science
Lifecycle
Problem &
Hypothesis
Design
Approach
Data
Acquisition
&
Exploration
Analysis &
Predictive
Modeling
Evaluation &
Reviews
Deployment
&
Socialization
Data Science Lifecycle
MS Doc: Team Data Science Process
Data Science
Lifecycle
Problem &
Hypothesis
Design
Approach
Data
Acquisition
&
Exploration
Analysis &
Predictive
Modeling
Evaluation &
Reviews
Deployment
&
Socialization
Data Science Lifecycle
MS Doc: Team Data Science Process
Data Science
Lifecycle
Problem &
Hypothesis
Design
Approach
Data
Acquisition
&
Exploration
Analysis &
Predictive
Modeling
Evaluation &
Reviews
Deployment
&
Socialization
Data Science Lifecycle
MS Doc: Team Data Science Process
Explore the underlying data
 Explore completeness, ranges, distributions
 Apply your sniff test
Tip #6: Unleash your curiosity
Use engineering standards
Make your work share-able &
re-usable:
 Source Control & Notebooks
 Data dictionary
 Data contracts & SLAs
 Privacy, Compliance, Ethics
Gather feedback to improve
your results:
 Peer & Code reviews
 Office hours & brownbags
 Stakeholder presentation &
action
 Publish
 Retrospectives
Tip #7: Role model quality approaches
Data Science in the real world
 Causation vs correlation
 Experiment considerations (time to market, opportunity cost, ethics)
 Done (and simple) are better than perfect
 80/20 rule
 Model explain-ability
 Skewed populations
 Value of data quality, monitoring and improving data sets
Tip #8: Prioritize practicality
Data Science
Lifecycle
Problem &
Hypothesis
Problem
Framing
Data
Acquisition
&
Exploration
Analysis &
Predictive
Modeling
Evaluation &
Reviews
Deployment
&
Socialization
Data Science Lifecycle
Scientists must speak
 Presentation skills
 Be concise
 Focus on the takeaways
 Connect with your audience
 Use volume, eye contact, pauses
 Practice
Tip #9: Land your message
Simplify for Impact
 Quiz: Which one is better?
Tip #11: Eliminate Distractions
Leverage Libraries
Credit: Cole Nussbaumer Knaflic
21
Grow your career
Hone your approach
• Become a SME
• Deliver results
Increase your impact
• Transform a space
• Share new ideas
• Help & represent the
team
Expand your horizons
• Mentoring, Network
• Books, Courses, Events
• Company & Industry
Stay in Touch
https://www.linkedin.com/in/cohenlisa/
https://medium.com/data-science-at-microsoft
Lisa.Cohen@microsoft.com
23
Q&A
© Copyright Microsoft Corporation. All rights reserved.

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Tips for Effective Data Science in the Enterprise

  • 1. Tips for Effective Data Science in the Enterprise Lisa Cohen
  • 2. 2 Session goals • Demystify Data Science Career Paths • Discuss best practices to tackle a Data Science Project • Gain Tips & Tricks for DS scenarios in the enterprise
  • 3. 2000 Applied Math Bachelor & Masters degrees Quantitative thinking, Applied sciences Cum laude 2004 VS Languages & IDE Technical Feature PM Building software, understanding customers, leading cross-functional feature team Incorporating SQL & data access into .NET programming languages Shipped VS 2005, 2008, 2010 Reached ~9M customers 3 patents for new designs 2012 Sr Mgr – VS Telemetry Product analytics: working with perf, compatibility, planning, privacy, compliance Led DevDiv business reviews, Advanced VS telemetry, Delivered VS active use clustering, Launched “Send A Smile” feedback 2015 Principal Data Scientist Mgr Using data to help customers succeed & grow on Azure. Driving DS best practices, Cross-MS partnerships Led cross-functional team. Evolved DS for credit offers, partners, Direct customers, Support. 2008 Sr Community PM - VS Divisional strategy, industry trends, Central org & systems, Cross group partners, Exec comms, CSAT & customer/ partner communities Led DevDiv blogs (3M+ views/mo), Presented keynotes & sessions at 50+ conferences YR Role Experience& Learnings Achievements
  • 4. Nurturing customers, growing the business, connecting sources and advancing the DS craft Students & Developers Student offer, VSE Direct / Unmanaged Individuals, SMC, Sign-Up Field & EA Enable Sales Cross Cloud, Support, Retention, Service Usage, Fraud, Payments Data Science, Machine Learning, ML Ops, Experimentation, Data Vis, PM Partners & Startups ISVs, CSP, PAL, DPOR, MfS Marketplace App Source Partner Center Customer Portal Azure Portal Advisor Cost Management Azure.com Docs Learn PORTALSAUDIENCES FUNDA- MENTALS CAP- ABILITY
  • 5.
  • 6. 6 Session goals • Demystify Data Science Career Paths • Discuss best practices to tackle a Data Science Project • Gain Tips & Tricks for DS scenarios in the enterprise
  • 7. Demystifying Career Paths  Roles: What excites you about Data Science? Data Scientist Analytics & Inference • Statistical analysis & experiments Machine Learning Scientist/Engineer Production Models • Develop predictive models, MLOps Data Engineer Data Platform & Pipelines • Build the data platform Program Manager Planning & Stakeholder Engagement • Manage the data science process Tip #1: Follow your passion
  • 8. The Data Science Venn Diagram  Technical  Analytical Problem Solving  Statistics  Querying  R, Python, SQL, Kusto  Big Data  Modeling  Data Visualization Technical Soft Skills Domain  Domain  Business context  Data sets  Soft Skills  Communication  Organization  Cross-Group Collaboration  Teamwork Conway’s Venn Diagram Tip #2: Chart your path
  • 9. Data Science Organizations  What kind of environment do you want to be in? CentralizedEmbedded A core data science org provides services to business or functional teams across the company as a center of excellence Individual data science teams are spread throughout the company, reporting to and serving specific business or functional teams Tip #3: Find a DS community Tip #4: Connect with Stakeholders
  • 10. 10 Project Intake Tips Kicking off a model, experiment or analytics project  What new capability will this enable?  What decision/action will you take?  What’s the expected impact? Planning Process Project Intake Questions Prioritization & Scalable Solutions Tip #5: Focus on what matters (Prioritize with stakeholders, ask questions, socialize results)
  • 11. Data Science Lifecycle Problem & Hypothesis Design Approach Data Acquisition & Exploration Analysis & Predictive Modeling Evaluation & Reviews Deployment & Socialization Data Science Lifecycle MS Doc: Team Data Science Process
  • 12. Data Science Lifecycle Problem & Hypothesis Design Approach Data Acquisition & Exploration Analysis & Predictive Modeling Evaluation & Reviews Deployment & Socialization Data Science Lifecycle MS Doc: Team Data Science Process
  • 13. Data Science Lifecycle Problem & Hypothesis Design Approach Data Acquisition & Exploration Analysis & Predictive Modeling Evaluation & Reviews Deployment & Socialization Data Science Lifecycle MS Doc: Team Data Science Process
  • 14. Explore the underlying data  Explore completeness, ranges, distributions  Apply your sniff test Tip #6: Unleash your curiosity
  • 15. Use engineering standards Make your work share-able & re-usable:  Source Control & Notebooks  Data dictionary  Data contracts & SLAs  Privacy, Compliance, Ethics Gather feedback to improve your results:  Peer & Code reviews  Office hours & brownbags  Stakeholder presentation & action  Publish  Retrospectives Tip #7: Role model quality approaches
  • 16. Data Science in the real world  Causation vs correlation  Experiment considerations (time to market, opportunity cost, ethics)  Done (and simple) are better than perfect  80/20 rule  Model explain-ability  Skewed populations  Value of data quality, monitoring and improving data sets Tip #8: Prioritize practicality
  • 17. Data Science Lifecycle Problem & Hypothesis Problem Framing Data Acquisition & Exploration Analysis & Predictive Modeling Evaluation & Reviews Deployment & Socialization Data Science Lifecycle
  • 18. Scientists must speak  Presentation skills  Be concise  Focus on the takeaways  Connect with your audience  Use volume, eye contact, pauses  Practice Tip #9: Land your message
  • 19. Simplify for Impact  Quiz: Which one is better? Tip #11: Eliminate Distractions
  • 20. Leverage Libraries Credit: Cole Nussbaumer Knaflic
  • 21. 21 Grow your career Hone your approach • Become a SME • Deliver results Increase your impact • Transform a space • Share new ideas • Help & represent the team Expand your horizons • Mentoring, Network • Books, Courses, Events • Company & Industry
  • 24. © Copyright Microsoft Corporation. All rights reserved.

Editor's Notes

  1. Careers are only built in retrospect You can’t connect the dots looking forward; you can only connect them looking backwards. So you have to trust that the dots will somehow connect in your future. You have to trust in something — your gut, destiny, life, karma, whatever. – Steve Jobs
  2. Org maturity levels
  3. Tips: Don’t feel limited by the boundaries Follow your passion and leverage your strengths Share your interests with your manager Take on projects that align with your future goals
  4. Tips: Make a plan for career experiences & learnings Tackle Imposter Syndrome Apply the Venn diagram to the organization or Data Science field Notes: Leverage the diversity of the team backgrounds, with group projects Have fun with the team. Help & contribute to each other.
  5. Pros/Cons & Tips: Pro: Drive product direction, product management Con/Tips: Join DS communities, Find a mentor Pro: More career paths, diverse projects, like-minded peers & team projects Con/Tips: Steering meetings, Joint planning, Join aliases for context, Find champs Notes: Grass is greener. All exist at Microsoft. MS is centralized at the product level
  6. Data Science maturity stages Partner vs serving ad hocs, bring new ideas, file work Saying no, moving replies out of email, office hours
  7. https://www.google.com/search?q=arc+arrow&tbm=isch&hl=en&chips=q:arc+arrow,g_1:blue:AlXoYtHtNkI%3D&rlz=1C1GCEU_enUS820US820&hl=en&ved=2ahUKEwjalJPLzfTpAhUzkZ4KHbMSAZIQ4lYoCHoECAEQJQ&biw=1479&bih=2261#imgrc=RX46V2bKlXmNmM&imgdii=wHBImvC5s7jK6M
  8. https://unsplash.com/photos/M-EwSRl8BK8 Bring together end-to-end datasets Gain context from source owners
  9. Use visual aids, make your message pop Make your visuals work for you, not against you
  10. https://pixabay.com/photos/raise-challenge-landscape-mountain-3338589/
  11. https://www.flaticon.com/free-icon/linkedin_174857 https://medium.com/@Medium