Data Science Leaders

AI leaders. Why do we need them? How do you become one? And above all, how do you keep your job as one?

In this episode, we are joined by guest speaker Mike Gualtieri, VP and Principal Analyst at Forrester, and we unpack the opportunities, pitfalls, and best practices of the AI leader role. He shares the pivotal role of AI leaders in catalyzing organizational transformation, their unique skill set that must encompass data science, business acumen, and software engineering, their importance in navigating the evolving regulatory landscape surrounding AI, and the need for platforms to facilitate rigorous auditing and compliance measures to foster trust and transparency.

Join us as we discuss:

• The strategic imperative for AI leaders to curate a diversified portfolio of AI initiatives 
• The multifaceted nature of AI risk management, spanning legal, ethical, and societal dimensions
• The formidable challenges inherent in navigating and enforcing AI-centric regulations amidst rapid technological advancement

GenAI is evolving at a breakneck pace, matched only by the startups that are looking to commercialize it. So what better way to understand the latest GenAI trends than to ask a venture capitalist specializing in AI? 

In this episode, we speak with James Cham, partner at Bloomberg Beta, about the state of GenAI – where it is delivering value today – and the challenges preventing firms from moving from incremental GenAI-driven productivity gains, to truly disruptive GenAI use cases. Along the way, we cover the problem of treating GenAI like software development, the rapidly changing economics of GenAI, and the key to success with all types of AI which is – as always – understanding people. 

Join us as we discuss:

• The cultural challenges preventing companies from unlocking the disruptive potential of GenAI
• Why developers don’t get data-powered applications (and why data scientists need product thinking) 
• How GenAI can change our engagement with technology (by killing the GUI)

A human being consists of billions of cells, each with the same genetic code but interacting in a myriad ways that can eventually translate into disease. Understanding and treating that disease is, in essence, a data problem. But how do you unlock that data and how do you change an organization to systematically use that data to improve decision-making and accelerate drug discovery? 

In this episode, we speak with Volodimir Olexiouk, Director of Scientific Engagement and Data Science Team Lead at BioLizard, about best practices for overcoming the data challenges for AI-driven drug discovery and combining scientific expertise with data science for augmented intelligence in the life sciences. 

Join us as we discuss:

• The challenges in discerning correlation from causation and integrating domain expertise
• How bridging expertise gaps and merging data silos in pharmaceutical companies radically improves drug-discovery processes 
• The promise AI holds for swifter and more effective responses to future pandemics

Trip planning may well be the perfect AI use case. Too much information, too many combinations, and too little time —for humans, but not for Tripadvisor’s AI Trips. In this episode Rahul Todkar, VP Head of Data and AI, shares the secrets to building a trusted GenAI solution at internet scale and discusses the similarities and differences between data leadership roles at digitally native companies and more traditional enterprises.

Join us as we discuss:

• How to use GenAI to unlock first party data
• The ideal GenAI development team
• The evolving role of data and AI leaders

Wouldn’t it be great if there was a commonly agreed-upon framework for executing all AI projects successfully? Well, there isn’t one. However, there is CRISP-DM, the antediluvian “Cross-Industry Standard Process for Data Mining”, but you need to expand, modernize and adapt this framework for success at your organization.

In this episode from the archive, Dave Cole interviews David Von Dollen, former Head of AI at Volkswagen of America, about how they integrated CRISP-DM into an Agile process to drive more rapid iteration and, ultimately, more successful AI projects.

What’s just as important as the government keeping us safe from AI? Government leveraging AI to keep us safe!

In this episode, we interview Joel Meyer – former head of strategy at the Department of Homeland Security (DHS) and the person who drove the creation of the DHS AI Task Force. Joel shares how they identified key areas where they could apply AI to improve national safety and security, such as combating fentanyl and child sexual exploitation and abuse, and the steps that the federal government is taking to build AI capabilities across the public sector.

Join us as we discuss:

• Key areas where US government agencies are looking to leverage AI to improve mission effectiveness
• The people, process, and technology steps that government agencies are implementing to scale AI and how they apply to the private sector
• The importance and value of Responsible AI in public sector use cases and beyond

There is no such thing as an AI drug, but AI and ML-models are driving the next wave of new treatments. In this episode, Brandon Allgood, Chief Data Officer at FogPharma and serial entrepreneur at the intersection of ML and Biopharma, shares his insights on how AI is disrupting the traditional process of drug discovery and development.

Join us as we discuss:

• Why AI is so powerful for drug discovery
• What data science needs to learn from engineering
• How drug discovery processes need to be rebuilt with AI models at their core

What’s the biggest problem in AI today? It’s that far too few projects make it to deployment. In this episode, Eric Siegel, founder of the long-running Machine Learning Week conference and creator of the first (and perhaps only) ML music video, tells us about his new book, The AI Playbook and the bizML framework for aligning stakeholders and maximizing the chance for deployment and impact.

Join us as we discuss:

• Causes behind the high rates of AI project failure
• Critical project steps for ensuring deployment
• Humor as a means to bridge the gap in AI understanding

And check out:

• The AI Playbook: Mastering the Rare Art of Machine Learning Deployment
• The entire Predict This music video. You won’t regret it.
• Machine Learning Week June 4-7, 2024

The biggest challenges to driving impact with AI have little to do with AI and everything to do with humans. Nowhere is this greater than with GenAI where myths and misconceptions abound as to how organizations should be designing, developing and operationalizing GenAI-based applications. In this episode with Rowan Curran, industry analyst at Forrester Research, we debunk the most harmful myths and discuss how AI teams are shattering these myths and delivering transformative outcomes.

Join us as we discuss:

• The role of data scientists and ML engineers in GenAI projects
• Successful approaches to prompt engineering
• The linkages between MLOps and LLMOps

Imagine Generative AI handling tens of thousands of conversations with your customers daily. Science fiction? Not at Bolt where this has been in production since the summer of 2023. In this episode, Mikhail Korolev – head of the data science team at Bolt’s food delivery service – shares the challenges and hard earned best practices for operationalizing a GenAI application that dramatically lowers cost while also increasing customer satisfaction.

Join us as we discuss:

• How to leverage GenAI to automate customer service conversations
• How to manage inconsistency and mitigate risk in GenAI apps
• How to protect sensitive data and comply with regulatory requirements with GenAI