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2. Capabilities for Leading Projects

2.1 Technology

A tech lead provides guidance in

Technology choices

Project execution tradeoffs

Business knowledge and contexts

Role is to deliver technical solutions with the available resources on time

Main challenges of setting up a data science project is around

Framing the problem to maximize business impact

⁠

A tech lead should recommend data science solutions that can make an outsized impact on the business

Discover patterns in data

Understanding data characteristics

Innovating in feature engineering

Clarifying the modeling strategy

Setting expectations for success

2.2 Execution

2.2.1 Specifying projects from vague requirements

Effective data science tech leads learn to ask the question behind the question.

Business partners can provide requests that may be sub-optimal framing of the problem

Need to ask why the request is important/beneficial to the business

Ways to prioritize projects

First based on business impact

Then refine by assessing

Reach: what population can/will be affected?

Impact: overall business impact

Confidence: what data is available/reliable

Effort: execution process

Finally, account for alignment to data strategy

2.2.2 Planning a data science project

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