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Idiots guide to setting up a data science team

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Idiots guide to setting up a data science team

  1. 1. 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
  2. 2. 2 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
  3. 3. 3 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
  4. 4. 4 Knowing What You Want is a Great First Step!
  5. 5. 5 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
  6. 6. 6 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 “ ”
  7. 7. 7 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
  8. 8. 8 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
  9. 9. 9 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
  10. 10. 10 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
  11. 11. 11 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
  12. 12. 12 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
  13. 13. Thank You

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