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2.1 Technology
A tech lead provides guidance in
Technology choices
Porject 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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