Why is ML is so poorly adopted in small organizations (hint: it’s not because they don’t have enough data)? In this episode, Kirsten Lum from Storytellers shares the patterns she has seen in small orgs that lead to a successful ML practice. We discuss how the job of a ML Engineer/Data Scientist is different in that environment and how end-to-end project management is key to adoption. Join the discussionChangelog++ members save 2 minutes on this episode because they made the ads disappear. Join today!Sponsors:The Changelog – Conversations with the hackers, leaders, and innovators of the software world Featuring:Kirsten Lum – Twitter, LinkedInChris Benson – Twitter, GitHub, LinkedIn, WebsiteDaniel Whitenack – Twitter, GitHub, WebsiteShow Notes: storytellers.ai Trello Something missing or broken? PRs welcome!

Kirsten Lum, an expert in non-technical backgrounds entering the world of analytics, shares strategies for success in the evolving analytical field. Topics include transitioning from literature to CTO, the importance of technical skills, evolution of analyst roles, data interpretation, embracing Python, risk-taking, happiness experiments, and a humorous pants mix-up.

Join Kirsten Lum, Chief Product Officer at Storytellers.ai, as she shares her career journey, challenges and success in bootstrapping her company, fascinating use cases of AI and ML, advice for aspiring data scientists, fostering innovation in tech teams, and exciting trends in analytics and AI.