There is a lot of confusion around the role of Data Science, what skills it requires, and what career paths it can lead to. This deck attempts to clear up some misconceptions, speaks to the different roles within data science, and discuss how individuals and data orgs can succeed in this nebulous environment.

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Data Update - 01/27/2016vsco.co/blevishkin
Data Update - 03/17/17vsco.co/prazakj
04 SEP 2018
RUBEN KOGEL ( VSCO )
The Role(s) of Data Science in
Modern Organizations

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Everybody’s confused on
what *is* Data Science
→ Execs (who “hire” the DS function) want
“insights” and tracking for everything
→ Orgs need a business analytics function, a
personalization function, or both
→ Candidates think it’s mostly about fancy
machine learning techniques
→ Practitioners think it’s mostly about
counting things properly
vsco.co/evanhundelt

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The confusion is on “data”
and who should analyze it
→Lots of types of data
• events / clickstream
• production data
• financials
• qualitative insights / feedback
• competitive intelligence
→Should every data collection, reporting, or
analysis go through Data Science?
vsco.co/evanhundelt

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“Data Science” really is one
of two functions / skillsets
→ (Business) Analytics
• serves internal customers
• data insights on how the business is
performing and how to improve it
• requires data analysis & business sense
→ Personalization
• serves external customers (via Product)
• optimizes user experience and LTV via
personalized results (using user data)
• requires data engineering skills (ML) &
experimental design
vsco.co/evanhundelt

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Analytics Customers
→Product
→Marketing
→Revenue
→Other: People Ops, Support, etc.

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Analytics Mission
Provide relevant, reliable and timely data insights to inform how the
business is performing and how to improve it, via:
• instrumentation
• ad-hoc analyses
• advisory
Goal: accelerate the feedback loop & drive faster, better decisions
that will yield better business outcomes

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How Analytics Succeed - at *relevant*
Every analysis has a business counterpart who can ask questions
and act on the findings
→collaboration - on the question, the findings - is key
→success is tied to the business counterpart success
→analytics org mirrors counterparts (product, marketing, revenue)

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How Analytics Succeed - at *timely* & *reliable*
Invest in systems & processes to prevent mistakes and increase
productivity
→standardize methods, data sources, metrics, reporting
→invest in clean, robust, and scalable data infrastructures
→document all important findings
→develop & promote self serve tools whenever possible
→hybrid centralized / de-centralized model is optimal

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How Data Analysts Succeed (individual level)
→Curious, persistent, & thorough mindset
→Rigorous analytical thinking
→Interpersonal & communication skills
→Coding skills (SQL + data manipulation language)
→Statistics and data intuition
→Business sense