

Product data science for Microsoft AI, data scientist's role of GenAI, how to deal with burn out - Sid Sharan - The Data Scientist Show #077
4 snips Jan 15, 2024
Sid Sharan, Senior Data and Applied Scientist at Microsoft, discusses evaluating AI products, OpenAI API for sentiment analysis, data science team culture, collaboration between data scientists and product managers, dealing with burnout, and the role of product data scientists in the age of generative AI tools.
AI Snips
Chapters
Transcript
Episode notes
Evaluating AI Product Viability
- Microsoft evaluates AI products based on the value they add to users and commercial viability.
- Integration into existing products and measurable user impact drives decision-making over hype.
Align Metrics and Collaborate
- Before experiments, align with teams on primary and guardrail metrics.
- Analyze segment data deeply and collaborate to avoid killing ideas prematurely.
Use Pre-Post for Complex Tests
- Use pre-post analysis when A/B testing is infeasible.
- Compare user metrics before and after feature launches over sufficient time for insights.