Idiot’s Guide to Creating a Data Science Practice

We Create Emotionally Powerful and

Economically Sound Brand Experiences

powered by Programmatic And Strategic

Content

Ashish Bansal

April 30th 2015

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Today’s objectives

Today I hope to convey to you that…

1. …data science team are built with a limited understanding of the

benefits

2. …it is very hard to find the right people for the role

3. …there are a few core things to build once the team gathered

I hope that you…

1. …improve your understanding of the role of data scientists in an org.

2. …learn how to increase your value in the market (get paid more!)

3. …find my jokes funny

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Why Does a Digital Marketing Agency Need a Data Science Practice?

We are founded on four core pillars –

Strategy, User Experience, Technology, and Data

We want to build programmatic experiences (not

programmatic media) and foster brand loyalty with strong

measureable RoI

We want to build our own IP, solve problems no one has

attempted before

We need data scientists who know marketing, and

marketers who understand data

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Knowing What You Want is a Great First Step!

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Knowing What You Want…

I want to do deep learning

We need to build a

recommendation system

All other companies in our

category have a data science

practice

I want to automatically categorize

content for more relevant search

We want to improve customer

retention and cross-sell more

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Knowing What You Want…

Have an end in mind – envision what success looks

like for your data science team

Be hypotheses driven – don’t fall into the trap of ‘lets

just look for something cool’

Simpler explainable algorithms before complicated

ones (given enough data)

Understand the domain…. Of the business you are in

“

”

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Data Science, Data Engineering, Big Data blah blah blah..

What is the difference between Computer Science and Computer Engineering? Do you NEED

computer scientists or computer engineers? Do you HIRE computer scientists or computer

engineers?

Big Data is inconsequential.

Today’s tools hide complexity of big, small, thick, thin, light, dark, wide data. Think of Hadoop as

operating system.

Pop Quiz: If Hadoop is OS, then how would Cloudera, HortonWorks and MapR map to

Microsoft, Mac OSX and Linux?

Differentiate between Exploratory Data scientists vs Operational Data Scientists

• Exploratory work requires challenging assumptions, learning very quickly, and moving on

to the next thing – Most likely bored by repetitive work, architecture/code quality is

immaterial

• Operational work focuses on large scale deployment of algorithms, getting rid of feedback

loops, good code and architecture, performance optimizations

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Data Scientist, Data Engineer….

Your options are:

• Hire one that can do both… (impossible to find)

• Hire a data scientist and a data engineer… (expensive)

• Hire one or the other and grow them into the other role… (takes

time)

Who do you need first?

• I need to prove that this team could add value, I need to build a

business case: Hire a data scientist

• I don’t know where all the data is – need to manage it properly

prior to analyzing it: Hire a data engineer

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About Hiring Unicorns…

Programmer

Statistician

Man with glasses and hair

Wears Cardigan over a tie

Marketer (or your domain)

Writer

Must own Converse sneakers

No beer belly

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Let’s Do Some Sampling Right Now!

N > 20

Every one who considers themselves to be a programmer, raise your

hands

Now, everyone who considers themselves a data scientist, know math

& statistics, or work on machine learning algorithms, keep your hands

up

Everyone who can put these two together and roll their own k-means

on Hadoop/Spark etc, keep your hands up

Everyone who can write a best seller, present to large audiences,

create decks for executive audiences, build D3 visualizations keep

your hands up

Everyone who understands the business/domain and customers of the

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How to Hire a Data Scientist/Engineer if you are not one?

Ben Horowitz’s advice*:

Don’t hire on look and feel

Don’t value lack of weakness rather than strength

How I Did It:

• Educated myself – great resources available now – Coursera, Big Data

University, THUG meetups, PoC, AWS free tiers

• Talked to experts in my network – what do they do, what problems are they

solving

• Got leeway from my organization to fail early and learn quickly

• Decided against hiring unicorns – would rather grow them

* From The Hard Thing About Hard Things by Ben Horowitz

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Applying Software Engineering to Data Science/Engineering Work Product

Layout, style, self documenting code

Refactoring Code

Debugging

Unit Testing (esp. stochastic processes)

Pipeline Jungles*

Handling Changes to The Matrix*

*Must Read: Machine Learning: high Interest Credit Card of Technical Debt: http://research.google.com/pubs/pub43146.html

Thank You