This document outlines how to setup a sustainable data science business function. It recommends: 1) Starting with a small, focused team of 2-6 generalist data scientists; 2) Clearly defining projects with goals, specs, progress meetings and knowledge sharing; 3) Systematizing the data pipeline and analyses with source control, code reviews and refactoring; 4) Ensuring effective communication with daily stand-ups, a task schedule, centralized communication tools and allowing time for focused work. The key is to define problems, access data, embrace failure, automate where possible and maintain an ongoing development cycle with corporate sponsorship and strong internal/external communication.

1. Tech Talks: How to Setup a Data Science Business Function
Jun 2015
www.applied.ai
How to Setup a Data
Science Business Function
Applied AI Tech Talk

2. ● We are data scientists:
○ variously quants, statisticians, actuarial & machine learning types
● We are consultants:
○ we do complex data analysis, predictive modelling etc
○ and we also help to do the soft stuff...
… enabling companies to learn from their data in a sustainable way
This is a totally biased talk

3. Like any collaborative business effort involving research & development,
a data science function should be built carefully in order to enable the
best expertise and technologies.
- Me, ~2 weeks ago
http://blog.applied.ai/how-to-build-a-data-science-business-function/
How to Setup a Data Science Business Function a.ka.
Making in-house Data Science sustainable

4. ● Including, for example:
Data Science is a broad discipline
one-off scenario-
specific modelling
exercises
on-line predictive
modelling of user
actions
regular analysis of
campaigns and
customer discovery
… and a significant amount of data acquisition, preparation, storage etc

5. ● To be sustainable and minimise
risk, we need to combine:
○ great people
○ advanced maths
○ scientific experimentation
○ software engineering
○ high-quality data
○ solid business practices
○ communication
The most important thing is communication
https://www.quora.com/How-could-the-Data-Science-Venn-Diagram-be-improved

6. 1. Setting up and sizing the team
2. Defining and operating projects
3. Systemising the data pipeline and analyses
4. Ensuring effective communication
… to help us make in-house Data Science sustainable
Four main areas to cover:

7. ● The practitioner will use a wide variety of tools to:
○ acquire, manipulate, store and access data efficiently
○ design surveys and scientific experiments to test hypotheses
○ undertake statistically valid analyses
○ implement high-quality, optimised predictive models
○ derive and communicate actionable insights
… requiring diverse skills covering database management, software
engineering, statistical analysis, machine learning, graphic design, ethics,
social responsibility, domain knowledge and communication.
1. Setting up and sizing the team
Data Scientists need a lot of skills!

8. ● But the days of hiring a single, unicorn-like, 'full-stack' data scientist are
pretty much gone, and probably never really existed.
1. Setting up and sizing the team
Don’t believe in unicorns

9. The team needs to be small, agile and focused:
● 2-6 data scientists is ample
● they should be proven generalists, team-players and pragmatists
● able to cope with vague requirements, messy data and high failure rates
“The first hire(s) should help get three things ready: your data; a clear problem
to be solved; and a process to evaluate the business impact of any new
solution".
- Simon Chan, Forbes, April 2015 http://www.forbes.com/sites/theyec/2015/04/30/how-to-do-your-
first-data-science-hire-right/
1. Setting up and sizing the team
Start with a small, focused team

10. Any piece of research or development likely to last more than a few
days and/or involve more than one person should have:
● A primary sponsor and a project leader
● A well defined goal (SMART), and a written spec
● Progress meetings to validate and update the plan, with full and frank
communication between major stakeholders
● Knowledge sharing upon completion
● Consider maintaining a basic RACI and risks & issues register.
2. Defining and operating projects

11. Automate good workflows and deal with technical debt:
● Understand and map the data 'pipeline'
● Stop when the models are good enough
● Encourage a systematic, shared approach to the creation of all machine
learning tools and analyses, with:
○ proper source control and documentation
○ code reviews & 'lunch and learn' seminar sessions
○ regular refactoring of algorithms, applications and data preparation
scripts where appropriate.
3. Systemising the data pipeline and analyses

12. Strong communication within & without the team is vital, helping to
ensure that projects stay on-track and issues are spotted early:
● Daily stand-up meetings (<10 mins), sharing immediate activities & issues
● An up-to-date communal task schedule - e.g. the Kanban methodology
● Simplified and centralised comms tech; move written discussions away
from email and towards wikis, message boards, and group chats Slack
● Try to allow data scientists / software engineers the time & space to get
into a productive flow state without meetings and interruptions.
4. Ensuring effective communication

13. ● Start with a small team of capable generalists and work hard to define the
business problems and success criteria, set timescales and to understand &
access the available data
● Allow for and embrace failure, give data scientists time and space to
research and experiment
● Specialise when necessary, automate where possible and embed into an
ongoing cycle of development, maintenance and support.
● Require a corporate sponsor with clout and encourage strong
communication within the team and the rest of the business
http://blog.applied.ai/how-to-build-a-data-science-business-function/
In review

14. Applied AI is a data science consultancy
We provide data-driven insights and solutions using applied artificial intelligence
www.applied.ai
Thank You
Any questions?