The document discusses the challenges of scaling data science within an organization, particularly focusing on rapid team growth and the need for efficient onboarding processes. It emphasizes the importance of a standardized toolchain and a virtual development environment to facilitate quick contributions and experimentation with evolving data tools. The document also highlights the benefits of version control, automated testing, and integrated publishing to maintain analysis quality and streamline workflows.

1. Engineering a Platform
Scaling DataScience

2. Who is DataScience?
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● Do you need…
● …insights about your data?
● …to hire or train data scientists to provide
actionable insights?
● …a platform for consuming cutting-edge
data science and publishing within your org,
without having data scientists doing ops
work?
● …to focus your data scientists on your core
business, while we provide models for LTV,
pricing, retention and more?
● Visit www.datascience.com!

3. Two Primary Challenges
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● Rapid Team Growth
● Team member onboarding
requires a low-risk, standardized
toolchain. Time to first
contribution is measured in
hours, not days or weeks.
● Dynamic Tooling Landscape
● Best-of-breed data tools are
always changing. Our culture and
platform encourage
experimentation and evaluation
of new tools and techniques.

4. Rapid Onboarding
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● A packaged virtual
development environment
● No wrestling with complex
system dependencies and
version compatibilities
● A clean starting point to
quickly retreat at any time
● Monitoring and diagnostics
● Scripted automation for
customizing per user

5. Rapid Onboarding
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● Upgrades are vetted in
advance prior to wide release
● Continuous integration
provides automated feedback
● Group chat pushes
institutional knowledge out
into searchable, company-
wide record.
● A culture of sharing and
demonstration

6. Dynamic Landscape
● Configuration management to
quickly compose systems based
on requirements
● Software tools are constantly
evolving. A robust virtual
environment promotes
experimentation and iteration.
● Think in terms of categories of
tools not specific techs.
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7. Dynamic Landscape
● Version control: track changes
to analysis over time. promote
reproducibility
● Automated testing: An
engineering approach to analysis
quality
● Integrated publishing: A
publishing workflow that closely
follows the underlying analysis
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8. 8
Thank you.
Questions?
We’re hiring.

9. Appendix: Rapid Onboarding
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10. Appendix: Dynamic Landscape
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11. Thank you.