Our guest today is Chanuki Seresinhe, head of Data Science at Zoopla,  a company which provides millions of users with access to properties for sale and for rent. In our conversation, we first talk about Chanuki’s PhD where she used machine learning to identify relationships between beautiful places and happiness. We then dive into Data Science at Zoopla and talk about Generative AI and other exciting projects that Chanuki is currently working on. Throughout the episode, Chanuki shares great insights on why ML projects fail, the importance of good metrics, switching companies and how to progress in your career. If you enjoyed the episode, please leave a 5 star review and subscribe to the AI Stories Youtube channel.Follow Chanuki on LinkedIn: https://www.linkedin.com/in/chanukiseresinhe/Follow Neil on LinkedIn: https://www.linkedin.com/in/leiserneil/  ————(00:00) : Intro(01:23) : How Chanuki got into the field(04:58) : AI to better understand happiness(16:37) : Generative AI (21:26) : Generative AI vs supervised learning(24:47) : Data Science at Zoopla(31:46) : The importance of good metrics(35:33) : Dealing with outliers(39:41) : Why ML projects fail(46:30) : Switching companies(48:42) : Bias (54:47) : Career advice

Rémi Ounadjela shares his experience working on shipment optimization at Amazon and data science for risk and safety at TikTok. He discusses the differences between working at TikTok, Google, and Amazon. Rémi also gives advice on how to land a data science job in big tech and common mistakes to avoid.

Barr Moses, Co-Founder and CEO of Monte Carlo, shares her inspiring journey from data strategy at Bain to building a pioneering data observability platform. She discusses her unique experiences in the Israeli Army and highlights the importance of teamwork and feedback in data science. The conversation dives into the critical need for reliable data, illustrated by a staggering $100 million loss due to a data error. Barr also explains the five pillars of data observability and emphasizes how data accuracy is vital for trust, especially in an AI-driven future.

Our guest today is Parul Pandey, Principal Data Scientist at H2O.ai, Kaggle Grandmaster (notebooks) & book author of “Machine Learning for High Risk Applications”. In our conversation, we first dig into Kaggle. Parul explains how she became a Grandmaster, shares tips about data analysis and discusses the pros of learning on Kaggle. The second part of the episode is around machine learning for high risk applications. We talk about the risks of using AI to make decisions, talk about interpretable algorithms and give advice on how to deploy robust models. If you enjoyed the episode, please leave a 5 star review and subscribe to the AI Stories Youtube channel.Machine Learning for High Risk Applications: https://www.oreilly.com/library/view/machine-learning-for/9781098102425/Kaggle notebook (gold medal), "Geek Girls Rising : Myth or Reality!": https://www.kaggle.com/code/parulpandey/geek-girls-rising-myth-or-reality/notebookBlog post on Explainable Boosting Machines: https://towardsdatascience.com/the-explainable-boosting-machine-f24152509ebbFollow Parul on LinkedIn: https://www.linkedin.com/in/parulpandeyindia/Follow Neil on LinkedIn: https://www.linkedin.com/in/leiserneil/  ————(00:00) : Intro(01:57) : How Parul got into AI(07:20) : Kaggle & Becoming a Grandmaster(13:52) : Advice for good data analysis(20:27) : Pros of learning on Kaggle(24:28) : ML for high risk applications(49:20) : Interpretable algorithms (55:28) : Deploying robust models(01:01:22) : Future of AI(01:05:47) : Career advice

Our guest today is Marijn Markus, Managing Data Scientist at Capgemini. In our conversation, we first talk about data analysis to model and visualise the spread of Ebola. We then dig into crime analysis back to when Marijn worked with the police in the Netherlands. Marijn also shares his thoughts on the importance of causal inference and the value that humans can add to AI algorithms. We then explore machine learning applied to the consulting sector and discuss the pros and cons of working as a Data Scientist in a consulting firm. Marijn talks about project FARM and explains how he helped farmers across the world using AI and Big Data. He then finally shares advice on how to have an impact on our world and progress in your career. If you enjoyed the episode, please leave a 5 star review and subscribe to the AI Stories Youtube channel.Follow Marijn on LinkedIn: https://www.linkedin.com/in/marijnmarkus/Follow Neil on LinkedIn: https://www.linkedin.com/in/leiserneil/  ————(00:00) : Intro(02:16) : Modelling the spread of Ebola(10:47) : Crime analysis (20:20) : Causal Inference(27:24) : Diversity(32:19) : AI in consulting(39:54) : project FARM(51:27) : Making the world a better place with data(01:00:15) : Career advice

Our guest today is Louis Bouchard, founder of the What’s AI Youtube channel & Towards AI. Louis is also a PhD student at the Quebec Artificial Intelligence Institute (Mila) founded by Yoshua Bengio.In our conversation, Louis first talks about What’s AI and explains how and why he launched his own Youtube channel. We then explore the world of Research and AI Algorithms. We discuss some of the latest techniques, chat about what intelligence actually means and debate on whether AI algorithms are actually intelligent or not. We also dive into research papers where Louis shares advice on how to easily read and understand technical content. Finally, Louis explains how he got into the field, talks about his PhD and shares more about applying computer vision for medical images.  If you enjoyed the episode, please leave a 5 star review and subscribe to the AI Stories Youtube channel.Follow Louis on LinkedIn: https://www.linkedin.com/in/whats-ai/What's AI Youtube channel: https://www.youtube.com/@WhatsAITowards AI: https://towardsai.net/Follow Neil on LinkedIn: https://www.linkedin.com/in/leiserneil/  ---(00:00) : Intro(02:16) : What’s AI Youtube Channel(08:11) : AI Algorithms(12:10) : AI & Intelligence(18:58) : How to read research papers(35:47) : Latent Diffusion(41:06) : How Louis got into the field(54:25) : Master’s research project(01:00:37) : Computer vision for medical imaging(01:12:24) : Career Advice

Our guest today is Miguel Fierro, Lead Data Scientist at Microsoft working in the personalisation team. In our conversation, we first dig into Miguel’s PhD in robotics and the connection between robotics and AI.  Miguel then shares a few stories on reverse learning to learn faster, how to switch fields, taking initiatives, the importance of diversity and the power of practical knowledge. We finally talk about Data Science at Microsoft where Miguel talks about his career path and learnings he got from some of the best in the field.  If you enjoyed the episode, please leave a 5 star review and subscribe to the AI Stories Youtube channel.Follow Miguel on LinkedIn: https://www.linkedin.com/in/miguelgfierro/Follow Neil on LinkedIn: https://www.linkedin.com/in/leiserneil/  ---00:00) : Intro(02:05) : How Miguel got into the field(10:30) : Robotics(16:03) : Taking initiatives(17:49) : Importance of Diversity(22:24) : Switching fields (27:30) : Reverse learning & Practical Knowledge(42:18) : Getting projects done(47:57) : Future of AI & Robotics(55:19) : Data Science at Microsoft(01:00:17) : Asking for help(01:11:52) : Miguel’s path @Microsoft(01:13:45) : Career advice

Our guest today is Greg Coquillo, Technology Manager at Amazon and startup investor. Greg is also a LinkedIn Top Voice in Technology & Innovation (2021) and Data Science & AI (2020).In our conversation, Greg first explains how he got into the field and why he is so excited about working in the AI space. He also shares his view on Data Science and the skills required to become a great Data Scientist. We then dig into his career focusing on Lonza and Amazon. Greg talks about a price optimisation model he built to increase Lonza’s profit margin by around 20%, generating additional millions in profit. He also shares his experience working as a Technology Manager at Amazon. We finally talk about ChatGPT, the future of AI and career advice.   If you enjoyed the episode, please leave a 5 star review and subscribe to the AI Stories Youtube channel.Follow Greg on LinkedIn: https://www.linkedin.com/in/greg-coquillo/Follow Neil on LinkedIn: https://www.linkedin.com/in/leiserneil/  ---(00:00) : Intro(02:28) : How Greg got into the field(07:35) : Why working in AI is exciting(10:20) : Data Scientists vs Product Managers(19:46) : What makes a Good Data Scientist ? (28:21) : Transitioning to Product Manager at Lonza(35:38) : Price optimisation model(47:26) : The power of starting simple !(53:05) : ChatGPT(58:46) : Technology Manager at Amazon(01:02:50) : The future of AI (01:05:43) : Career advice

A 14-year-old CEO talks about coding since age 5, founding two companies, developing an education app on US presidents, delving into AI, machine learning, IoT, and future tech sectors. Embracing failure in education and balancing childhood while pursuing success as a young CEO.

Our guest today is Adam Sroka, Founder and Director at Hypercube Consulting.In our conversation, Adam first explains how he got into the field. He then shares management mistakes and advice from his experience at Incremental Group where he transitioned from Senior Data Scientist to Data & AI Director. We then dig into ML applied to the energy sector and talk about Adam's new company: Hypercube Consulting. We finally discuss data strategy, how to build strong data teams and the importance of writing online.   If you enjoyed the episode, please leave a 5 star review and subscribe to the AI Stories Youtube channel.Follow Adam on LinkedIn: https://www.linkedin.com/in/aesroka/Follow Neil on LinkedIn: https://www.linkedin.com/in/leiserneil/  ---(00:00) : Intro.(02:06) : How Adam got into Data Science.(07:38) : ML in startups.(20:12) :  From senior DS to AI Director @Incremental Group.(27:18) : Management mistakes & advice.(35:38) : ML applied to the energy sector @Origami.(44:21) : Building a data consulting company. (49:04) : Data strategy.(55:24) : Building strong data teams .(59:17) : Career advice.