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Welcome to the Data Science Talent blog, where you’ll find regular posts and content on the things we think you want to read and hear about in the world of Data Science & AI.

You’ll also find useful information and guides that will help you enhance your professional profile and develop your skills and strengths.

We aim to create a community of Data Science and AI practitioners and provide them with the tools to succeed in this ever-changing sector.

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Key Principles for Delivering AI Outcomes in Large Enterprises By Walid Mehanna

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Using Semantics to Find Trends in Global Quality Data By Rex VanHorn

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How SMEs Can Navigate the Data & AI Landscape By Frank Stadler

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Creating Value at Scale with AI in Pharmaceuticals By Tomislav Ilicic

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Enhanced Large Language Models as Reasoning Engines By Anthony Alcaraz

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From RAG to RAR By James Duez

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Essential Soft Skills for Data Scientists By Sandro Saitta

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Data Quality in Relation to Algorithmic Bias By Gareth Hagger-Johnson

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Common-Sense Leadership By Claus Villumsen

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Davos 2024: Rebuilding Trust, Reclaiming Relevance and the Catalytic Effect of AI By Zana Aston

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The Imperative of AI Constitutionalism: Building an Ethical Framework for a Brave New World By Sahaj Vaidya

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Challenges and Opportunities in Clinical Trial Registry Data By Philipp Diesinger

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Why Generative AI Projects Fail By Colin Harman

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Fortifying Digital Frontiers: Wise’s Innovative Approach to Fraud Protection By Iaroslav Polianskii and Luca Traverso

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Industry Case Study: Merck By Walid Mehanna

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Artificial Intelligence for Artificial Languages By Francesco Gadaleta

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The 5 Use Cases for Enterprise LLMs By Colin Harman

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Data Science City: Warsaw

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Could Rust Be the Future of AI? By Francesco Gadaleta

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Rise of the Data Generalist: Smaller Teams, Bigger Impact By Tarush Aggarwal

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Ethical and Sustainable Data By Sarah-Jane Smyth & Jasmine Grimsley

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AI at Continental By Filipa Castro

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Time Series Forecasting with Deep Learning in PyTorch (LSTM-RNN) By Zain Baquar

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Using Open Sourced LLMs in Language By Bartmoss St Clair

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Transforming Freight Logistics with Advanced Machine Learning By Luís Moreira-Matias

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A Journey from Data Analyst to Data Science Leader By Rebecca Vickery

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Navigating the Future of Ads: Large Language Models and Their Impact on Programmatic StrategiesBy Javier Campos

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Are Large Language Models the Ultimate Database? By Francesco Gadaleta

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Measuring the Effect of Change on Physical Assets By Colin Parry

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Leveraging Data for a Sustainable Competitive Advantage with GenAI By Michel de Ru

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AI the World Can Trust By James Duez

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Scalable Data Management in the Cloud By Jacques Conradie

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With LLMs, Enterprise Data is Different By Colin Harman

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The Path to Responsible AI By Julia Stoyanovich

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The Biological Model By Pat McQuillan

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The AI Revolution vs the Mobile Revolution By Rex Woodbury

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The Dawn of Synthetic Data By Philipp M Diesinger

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Data Science Platforms for Patient-Centred Drug Discovery By Benjamin Glicksberg

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How Infusing Marketing with Data Science Can Lead to Better Marketing Decisions By Faisal Wasswa

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Should You Industrialise your Data Science MVP? By Philipp M Diesinger

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The Principles of an Effective Data-Driven Organisation By Pat McQuillan

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Data Science City: London

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ChatGPT By Francesco Gadaleta

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What Is Causal Inference and Why Should Data Leaders and Data Scientists Pay Attention? By Graham Harrison

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How I Became a Heroine in Tech – A Personal Reflection By Anna Litticks

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Risk Mitigation and the Future of Large Language Models in the Enterprise Market By Damien Deighan

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Start Up: Interview with Tanmaiyii Rao

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Digital Twins: The First Fruit of the Metaverse, Powered by Generative AI By Aakash Shirodkar

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Building a Feature Platform Guided by Your Ethos By Andreu Mora

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How AI Is Driving the Eradication of Malaria By Arnon Houri-Yafin

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Embracing Neurodiversity: My Journey with ADHD and How Generative AI Propelled My Career in Data Science By Elle Neal

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Charting the Evolution of Talent in Data Science at the Event Horizon of AGI By Lin Wang

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From Humans to Hybrids: Preparing Your Workforce to Thrive in the Future By Philipp M Diesinger

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Rapid And Budget-Friendly LLM Training Using Lora: Francesco Gadaleta

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Interactive Insights: When Generative AI Meets Analytics BY Martijn Bauters

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LLMs, ELMs And The Semantic Layer By Tarush Aggarwal

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Evolution of Data Platforms: Francesco Gadaleta

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Start Up: Interview With Tamanna Haque – Getting Into Data Science

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Data Platforms – How Can The Right Data Platforms Streamline Your Organisation? Tarush Aggarwal

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Anna Litticks: Automisation Means Change

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Amsterdam – Data Science City: 7 Things About Amsterdam You May Not Know

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How To Scale Data Science Pilots To Industrialise Your Data: Philipp M Diesinger

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Nicole Janeway Bills: Ten Books To Expand Your Worldview As A Data Professional

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Should You Reassess Your Candidate Assessment In The Wake Of CHAT GPT3? By Damien Deighan 

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Heidi Hurst: A Guide To Satellite Imaging

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Dr Anna Litticks: Things I Wish I knew Before Becoming a Data Scientist

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Start Up – Ryan Kearns: How I Got Into Data Science

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Detecting Financially – Vulnerable Customers At Scale: Rebecca Vickery

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Data Science Can Be a Complex Place. Here We Try To Simplify It.

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We Explore The Fascinating World of Reinforcement learning with One of The Experts in The Field: Francesco Gadaleta

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Damien Deighan: Talks About The Current State Of Data Science Hiring

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Tarush Aggarwal: Tells us How he Got Started in Data Science at Salesforce And Wework

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Skanda Vivek: Senior Data Scientist From Onsolve Talks About How he Transitioned From Academia to The Data Science Industry

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Data Science City: Our First City Spotlight Highlights a Metropolis Rapidly Emerging as a Dominant Force in Data Science And AI on a Global Scale –  Frankfurt.

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Tam Tran: Shares Insights on How to Fix Common Cross-Validation Pitfalls

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Mikkel Dengsoe: A highly Respected and Experienced Data Leader, Sheds Light on The Complex Relationship Between Team Size And Data Complexity

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Genevieve Hays: Tells Data Scientists – If You Want to be Valued Then You Need to Ask The Right Questions

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Unlocking The Power of Neutral Networks in Machine Translation With Philipp Koehn

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