Abstract: With the October 2019 Report from MIT Sloan and Boston Consulting Group citing “seven out of ten companies report minimal or no impact from AI so far”, data science teams are under tremendous pressure to overcome these hurdles and demonstrate impact. In the 2017 Kaggle State of Data Science and Machine Learning Survey, based on over 16,000 responses, aside from dirty data, all of the hurdles faced by data science teams relate to organizational and stakeholder management issues.
No amount of Python or R code will solve such challenges. Instead, data science teams need to immediately employ product management practices to engage stakeholders effectively and demonstrate their value to the organization. Below are some simple steps Data Science teams can put into practice
My talk will focus on how to apply Product Management principles to managing data science, both in terms of the work product and also how to engage stakeholders.
The topics that I will cover:
1) Overview of challenges
2) Jobs to be done framework
3) Agile data product development
4) How to get started right away
You can find the article I published in Towards Data Science on this topic here:
Bio: Richard leads a global team of Data Scientists, ML Ops Engineers, Backend Developers, Analysts and BI Developers as Global Director of Data Science & AI Platform at Z-Tech (part of AB InBev). Together, they are building data-driven technology solutions to small businesses around the world.
Richard has 12+ years of experience developing data products for startups and Fortune 500 companies. Before Z-tech, Richard headed up Data Analytics & Products at venture-backed ExecOnline Inc. Prior, he led the Machine Learning Platform team at Wayfair and before that he co-founded two ventures, including a Techstars HR Technology AI startup. Richard is a seasoned leader drawing on experience in Product Management, Corporate Strategy and Consulting. He has an MBA from NYU Stern and Bachelors of Engineering from SFU.