DataFramed

• #290 Scaling Responsible AI Literacy with Uthman Ali, Global Head of Responsible AI at BP

  The rise of AI tools has democratized access to technology, but with it comes the responsibility to use these tools ethically. How do organizations ensure their employees are not only aware of AI's capabilities but also its risks? What does it mean to have a responsible AI strategy that is both comprehensive and adaptable to future advancements? As companies strive to align their AI initiatives with ethical standards, what are the best practices for training and upskilling teams to meet these challenges head-on?Uthman Ali is the Global Head of Responsible AI at BP and is an expert on AI ethics. As a former human rights lawyer and neuro-ethicist, he recognized how regulations were not keeping up with the pace of innovation and specialized in this emerging field. Some of his current projects include creating ethical policies/procedures for the use of robots, wearables and using AI for creativity.In the episode, Adel and Uthman explore the importance of responsible AI in organizations, the critical role of upskilling, the impact of the EU AI Act, practical implementation of AI ethics, the spectrum of AI skills needed, the future of AI governance, and much more.Links Mentioned in the Show:Report: The State of Data & AI LiteracyConnect with UthmanCourse: Responsible AI PracticesRelated Episode: Scaling AI in the Enterprise with Abhas Ricky, Chief Strategy Officer at ClouderaSign up to attend RADAR: Skills EditionNew to DataCamp?Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business

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• #289 How I Nearly Got Fired For Running An A/B Test with Vanessa Larco, Former Partner at New Enterprise Associates

  The rise of A-B testing has transformed decision-making in tech, yet its application isn't without challenges. As professionals, how do you navigate the balance between short-term gains and long-term sustainability? What strategies can you employ to ensure your testing methods enhance rather than hinder user experience? And how do you effectively communicate the insights gained from testing to drive meaningful change within your organization?Vanessa Larco is a former partner at NEA where she led Series A and Series B investment rounds and worked with major consumer companies like DTC jewelry giant Mejuri, menopause symptom relief treatment Evernow, and home-swapping platform Kindred as well as major enterprise SaaS companies like Assembled, Orby AI, Granica AI, EvidentID, Rocket.Chat, Forethought AI. She is also a board observer at Forethought, SafeBase, Orby AI, Granica, Modyfi, and HEAVY.AI. She was a board observer at Robinhood until its IPO in 2021. Before she became an investor, she built consumer and enterprise tech herself at Microsoft, Disney, Twilio, and Box as a product leader.In the episode, Richie and Vanessa explore the evolution of A-B testing in gaming, the balance between data-driven decisions and user experience, the challenges of scaling experimentation, the pitfalls of misaligned metrics, the importance of understanding user behavior, and much more.Links Mentioned in the Show:New Enterprise AssociatesConnect with VanessaCourse: Customer Analytics and A/B Testing in PythonRelated Episode: Make Your A/B Testing More Effective and EfficientSign up to attend RADAR: Skills Edition - Vanessa will be speaking!New to DataCamp?Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business

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• #288 How Generative AI is Transforming Finance with Andrew Reiskind, CDO at Mastercard

  Generative AI has transformed the financial services sector, sparking interest at all organizational levels. As AI becomes more accessible, professionals are exploring its potential to enhance their work. How can AI tools improve personalization and fraud detection? What efficiencies can be gained in product development and internal processes? These are the questions driving the adoption of AI as companies strive to innovate responsibly while maximizing value.Andrew serves as the Chief Data Officer for Mastercard, leading the organization’s data strategy and innovation efforts while navigating current and future data risks. Andrews's prior roles at Mastercard include Senior Vice President, Data Management, in which he was responsible for the quality, collection, and use of data for Mastercard’s information services and advisory business, and Mastercard’s Deputy Chief Privacy Officer, in which he was responsible for privacy and data protection issues globally for Mastercard. Andrew also spent many years as a Privacy & Intellectual Property Council advising direct marketing services, interactive advertising, and industrial chemicals industries.Andrew holds Juris Doctor from Columbia University School of Law and has his bachelor’s degree, cum laude, in Chemical Engineering from the University of Delaware. Andrew is a retired member of the State Bar of New York.In the episode, Adel and Andrew explore GenAI's transformative impact on financial services, the democratization of AI tools, efficiency gains in product development, the importance of AI governance and data quality, the cultural shifts and regulatory landscapes shaping AI's future, and much more.Links Mentioned in the Show:MastercardConnect with AndrewSkill Track: Artificial Intelligence (AI) LeadershipRelated Episode: How Generative AI is Changing Leadership with Christie Smith, Founder of the Humanity Institute and Kelly Monahan, Managing Director, Research InstituteSign up to attend RADAR: Skills EditionNew to DataCamp?Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business

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• #287 Self-Service Generative AI Product Development at Credit Karma with Madelaine Daianu, Head of Data & AI at Credit Karma

  As businesses collect more data than ever, the question arises: is bigger always better? Companies are beginning to question whether massive datasets and complex infrastructures are truly delivering results or just adding unnecessary costs. How can you align your data strategy with your actual needs? Could focusing on smaller, more manageable datasets improve efficiency and save resources while still delivering valuable insights?Dr. Madelaine Daianu is the Head of Data & AI at Credit Karma, Inc. Before joining the company in June 2023, she served as Head of Data and Pricing at Belong Home, Inc. Earlier in her career, Daianu has held numerous senior roles in data science and machine learning at The RealReal, Facebook, and Intuit. Daianu earned a Bachelor of Applied Science in Bioengineering and Mathematics from the University of Illinois at Chicago and a Ph.D. in Bioengineering and Biomedical Engineering from the University of California, Los Angeles.In the episode, Richie and Madelaine explore generative AI applications at Credit Karma, the importance of data infrastructure, the role of explainability in fintech, strategies for scaling AI processes, and much more.Links Mentioned in the Show:Credit KarmaConnect with MaddieSkill Track: AI Business FundamentalsRelated Episode: Effective Product Management for AI with Marily Nika, Gen AI Product Lead at Google AssistantSign up to attend RADAR: Skills EditionNew to DataCamp?Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business

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• #286 Data Science Trends from 2 Kaggle Grandmasters with Jean-Francois Puget, Distinguished Engineer at NVIDIA & Chris Deotte, Senior Data Scientist at NVIDIA

  With AI agents and GPU acceleration at the forefront, data science is entering a new era of efficiency and innovation. How are AI copilots transforming the way data scientists code and solve problems? Are they a reliable partner or a source of new complexities? On the other hand, the move to GPU-accelerated data science tools is revolutionizing model training and experimentation. What does this mean for the future of data science workflows? Explore these cutting-edge developments and their impact on the industry.Jean-Francois got a PhD in machine learning in the previous millennium. Given the AI winter at the time, he worked for a while on mathematical optimization software as dev manager for CPLEX in a startup. He came back to Machine Learning when IBM acquired the startup. Since then he discovered Kaggle and became one of the best Kagglers in the world. He joined NVIDIA 5 years ago and leads the NVIDIA Kaggle Grandmaster team there.Chris Deotte is a senior data scientist at NVIDIA. Chris has a Ph.D. in computational science and mathematics with a thesis on optimizing parallel processing. Chris is a Kaggle 4x grandmaster.In the episode, Richie, Jean-Francois, and Chris explore the transformative role of AI agents in data science, the impact of GPU acceleration on workflows, the evolution of competitive data science techniques, the importance of model evaluation and communication skills, and the future of data science roles in an AI-driven world, and much more.Links Mentioned in the Show:NVIDIANVIDIA RapidsFew shot learningConnect with Jean-Francois on Linkedin and Kaggle and check out Chris on KaggleCourse: Winning a Kaggle Competition in PythonRelated Episode: Becoming a Kaggle GrandmasterSign up to attend RADAR: Skills EditionNew to DataCamp?Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business

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