Building Value into the Data Science Org | Online

About this event

Dataiku is excited to kick off their monthly talk series with two presentations on the importance of collaboration and awareness in data science, featuring speakers from Salesforce and Dataiku!

Tentative Schedule:

• 6:30pm: Pizza + Beer networking
• 7:00pm: Doing Data Science Successfully: Dreaming big at a startup or starting at a massive corporation with Yashas Vaidya, Data Scientist at Dataiku
• 7:30pm: AI’s impact on the PM role and what it means for Data Scientists by Mayukh Bhaowal, Director of Product Management at Salesforce Einstein

Talk Abstracts:

Doing Data Science Successfully with Yashas Vaidya, Data Scientist at Dataiku: Data Science, ML and AI and/or advanced analytics are initiatives taking place at every company whatever their size. Whether they're data-driven startups or large companies just starting up their advanced analytics, everyone is doing it. These initiatives are often seen as the path to ensuring success in promoting or preventing disruption. Unfortunately, many of these initiatives face obstacles, because they are not embedded within various levels of the organization. This talk will discuss what it means for data science to be embedded within an organization, common failures without such embededdness and steps to ensure success. It will cover lessons learnt from experience working with organizations large and small that are building and deploying complex data-enabled, ML-powered and AI-driven data products.

AI’s impact on the PM role and what it means for Data Scientists by Mayukh Bhaowal, Director of Product Management at Salesforce Einstein:

There is an evolution in Product Management correlated with the shift from the digital revolution to the AI revolution. As AI and ML eat software, more and more PMs need to level up their skills to manage these products and provide requirements and specifications which will add value to the data engineering and data science teams. This will lead to actually solving customer pain points and not just building a cool technology solution.

In addition to working with their traditional cross-functional stakeholders (design, marketing, sales, engineering, dev ops), AI product managers now need to include data scientists and data engineers in the circle.

Mayukh Bhaowal discusses the top areas AI product managers and Data Scientists/Data Engineers need to collaborate on, including mapping of business problems to machine learning problems, understanding data and labeled data nuances, defining crisp model evaluation criteria, model explainability, ethics/bias and the distinction between research and production when it comes to AI powered products and features.