Data Science Talent

We are excited to announce the launch of Issue 7 of The Data Scientist Magazine TODAY! We want to express our deepest gratitude to our contributors, who made this issue possible. They are: - Francesco Gadaleta, Ph.D. from Amethix Technologies - Artificial Intelligence for Artificial Languages - Iaroslav Polianskii from Wise - Enhanced Aspects of Fraud Protection - Colin Harman from Nesh - Why Generative AI Projects Fail - Dr. Philipp M. Diesinger from Rewire - Challenges and Opportunities in Clinical Trial Registry Data - Sahaj Vaidya from New Jersey Institute of Technology - The Imperative of AI Constitutionalism: Building an Ethical Framework for a Brave New World - Zana S Aston EMBA from Emrys Group - Davos 2024: Rebuilding Trust, Reclaiming Relevance and the Catalytic Effect of AI - Gareth Hagger-Johnson from The Nottingham Building Society - Data Quality in Relation To Algorithmic Bias - Sandro Saitta from viadata - Essential Soft Skills for Data Scientists - Claus Villumsen from Kodebaze - Common Sense Leadership - James Duez from Rainbird Technologies - From RAG to RAR: Advancing AI with Logical Reasoning and Contextual Understanding - Anthony Alcaraz from Fribl - Enhanced Large Language Models as Reasoning Engines Thank you for your contribution to the Data Science community. SUBSCRIBE FOR FREE https://lnkd.in/e7fRDtrX Follow us on hashtag #DataScienceTalent #TheDataScientistMagazine #Issue7 #AI.

Issue 7 of The Data Scientist Magazine is out now! Read insights and recommendations from some of the industry’s top experts… But don’t just take it from us - we’ve received lots of great feedback from readers and companies across the globe who’ve enjoyed the magazine since its launch in November 2022. Subscribe here for free and let us know what you think: https://lnkd.in/epUC8C3F

You can find your ideal candidate in 48 hours*. Guaranteed. Our unique DST Profiler® system will help you hire the most suitable Data Scientists and Engineers by ensuring you get the right type of skills and experience for the job. - We recruit contractors to cover: - Skills or domain knowledge gaps - Fixed-term projects and transformation programmes - Maternity/paternity leave cover - Sickness leave cover Unexpected leavers/resignations. * In the first two weeks - If we provide you a contractor who is not a fit, we will replace them immediately and we won’t bill you. To learn more contact us here: Tel: +44 808 164 0995 Email: info@datasciencetalent.co.uk #DataScienceTalent #Guarantee

Issue 7 of The Data Scientist Magazine was launched on 21st May. If you haven't already subscribed, then we invite you to subscribe for free; https://lnkd.in/eQzwzAFM. Our team worked hard to produce a high-quality publication, which includes insightful articles from our top data experts about the progress of generative AI. In this issue, we investigate some of the potential applications and current limitations of GenAI. While generative AI has the potential to benefit organizations, it requires extensive work to deliver significant enterprise value. Our cover story features Francesco Gadaleta, who delivers an excellent piece on how and why LLMs are promising when used in conjunction with artificial languages in the area of robotics. Additionally, we have two features on knowledge graphs from James Duez and Anthony Alcaraz that come from different angles. Colin Harman is also back again with a superb summary of why certain GenAI projects fail. We continue to provide you with content on the latest developments in industry from the world of traditional data and machine learning approaches. The Fraud Protection team at the Forex technology company Wise have produced a high-quality deep dive into their approach to fraud protection across international boundaries. Meanwhile, Gareth Hagger-Johnson from The Nottingham Building Society brings much-needed attention to the often overlooked role of data quality in algorithmic bias. Dr. Philipp M. Diesinger and his team have provided an excellent overview of the current challenges that exist in the realm of clinical trials data, which directly impacts the success or failure of a large number of clinical trials every year. Away from the technical side of data and AI, we have two excellent articles in this issue that focus on the bigger picture of the development of AI. Zana S Aston EMBA & Georgios Sakellariou give us a rundown of which AI topics were hot on the agenda with world leaders and business leaders at Davos, with sustainability and the need to build trust front and center. Sahaj Vaidya explains AI constitutionalism and how that can help us make sure that future AI systems are ethical. Lastly, Claus Villumsen has written a great overview of what a leader of technology teams needs to get right in order to deliver high-performance teams. Data leaders need to have strong communication skills, and Sandro Saitta tells you what you need to know about how to communicate well as a data professional. We have received incredible feedback from our readers worldwide, which is a testament to the team's dedication. If you enjoy this issue of the magazine and would like us to feature you or your company in our next issue, we would love to hear from you. I appreciate your support. Imtiaz Deighan (Manager & Designer of The Data Scientist Magazine)

In Issue 7 of The Data Scientist Magazine, we delve into the world of AI with Anthony Alcaraz, Chief AI Officer and Partner at Fribl. Anthony's work focuses on integrating advanced AI solutions into HR, optimizing candidate evaluation, and ensuring ideal job matches. His expertise extends to decision science, large language models, natural language processing, knowledge graphs, and graph theory, positioning him as a leading voice in technologies like retrieval-augmented generation (RAG) and reasoning engines. The article explores Enhanced Large Language Models as reasoning engines, highlighting the potential of LLMs to achieve human-level intelligence. While they excel in generating coherent text, challenges persist in tasks requiring abstract reasoning. Neural networks struggle with causal, counterfactual, and compositional reasoning, lacking the structured symbolic representations that enable systematic composability and causal models crucial for human cognition. Acknowledging the 'hybridity gap' between neural approaches and structured reasoning is vital for progress. Embracing the strengths of both neural networks and structured knowledge representations is key to developing integrated reasoning systems. To read the full article, subscribe for free: https://lnkd.in/exgtbfYW #TheDataScientistMagazine #AI #DataScience #ArtificialIntelligence #HR #Technology #Innovation #NeuralNetworks

In Issue 7 of The Data Scientist Magazine we are pleased to present James Duez, CEO and co-founder of Rainbird Technologies.AI, sharing insights on advancing AI with logical reasoning and contextual understanding in his article "FROM RAG TO RAR." James brings over 30 years of experience in building and investing in technology companies, focusing on global compliance, enterprise transformation, and decision science. He has collaborated with Global 250 organisations and state departments, and is recognized as one of Grant Thornton’s ‘Faces of a Vibrant Economy’ and a member of the Forbes Technology Council. In his article, James emphasises the importance of a Neurosymbolic approach to decision intelligence, highlighting the significance of solutions grounded in trust and transparency to avoid risks associated with generative AI technology. To delve deeper into James's insights on leveraging AI and data effectively, subscribe for free to; https://lnkd.in/eA6KN7tu #TheDataScientistMagazine #Neurosymbolic #Transparency

You can find your ideal candidate in 48 hours*. Guaranteed. Our unique DST Profiler® system will help you hire the most suitable Data Scientists and Engineers by ensuring you get the right type of skills and experience for the job. - We recruit contractors to cover: - Skills or domain knowledge gaps - Fixed-term projects and transformation programmes - Maternity/paternity leave cover - Sickness leave cover Unexpected leavers/resignations. * In the first two weeks - If we provide you a contractor who is not a fit, we will replace them immediately and we won’t bill you. To learn more contact us here: Tel: +44 808 164 0995 Email: info@datasciencetalent.co.uk #DataScienceTalent #Guarantee

In our recent blog post, Faisal Wasswa, Lead Marketing Data Scientist at NatWest Group, discusses the impact of infusing marketing with data science to improve marketing decisions. Faisal holds a BSc in Economics from Brunel University London and an MSc in Financial Economics from Birkbeck, University of London, and specialises in Econometrics, Python, R, Eviews, and SAS. Faisal explains how data science can enhance a company’s marketing strategy, making it more cost-efficient and targeted. He highlights the power of data science in measuring every aspect of marketing, from competitor analysis to media spend. Faisal refers to the famous quote by John Wanamaker in the 1920s, highlighting the challenge of not knowing which portion of advertising spending is ineffective. He also mentions Martin Sorrel, the previous CEO of WPP, who stated that clients are potentially wasting 15-25% of their advertising budgets without knowing which specific part. You can read the full blog here; https://lnkd.in/eChVHRfz

We are thrilled to introduce Sandro Saitta in Issue 7 of The Data Scientist Magazine for his insightful article on ESSENTIAL SOFT SKILLS FOR DATA SCIENTISTS. Sandro, currently AI Advisor at viadata, boasts 20 years of data science experience, working across industries like telecommunications, chemicals, online travel, and FMCG. He is dedicated to helping companies embrace data-driven strategies and serves as a lecturer at Business School Lausanne and HEC Lausanne. In his article, Sandro emphasises the significance of soft skills in the realm of data science. While technical skills are pivotal, enhancing soft skills like data visualization, communication, stakeholder management, adaptability, and business acumen can magnify the impact of data initiatives within organisations. As data professionals, our academic training often focuses on technical aspects, neglecting the importance of soft skills. Sandro's article sheds light on how improving these skills can amplify our influence within companies, from framing questions effectively to gaining stakeholder trust and buy-in for data projects. Remember, the success of data projects hinges not only on analysis but also on effective communication and stakeholder engagement. By honing soft skills, data scientists can navigate challenges and drive meaningful outcomes in the ever-evolving landscape of data science. To read the full article, we invite you to subscribe for free at https://lnkd.in/eA6KN7tu. #TheDataScientistMagazine #DataScienceTalent #SoftSkills

Our recent blog post by Dr. Philipp M. Diesinger explores the question of whether you should industrialise your data science MVP. With a rich background in data science, Philipp highlights the challenges companies encounter when scaling minimal viable products (MVPs). With over a decade of experience in key roles within the field, including prestigious positions at MIT, SAP, and Boehringer Ingelheim, Philipp brings a wealth of expertise to the table. His expertise in Predictive Analytics and machine learning is evident in his analysis. In his post, Philipp emphasises the complexities involved in industrialising and scaling MVPs. While the ability to prototype data science projects rapidly has advanced significantly in recent years, the industrialisation process remains intricate, requiring coordination among various stakeholders and substantial resources. Organisations are faced with critical decisions on which MVPs to industrialise, recognising the importance of strategic choices in this process. Philipp's insights shed light on the multifaceted nature of this transformation, urging companies to adopt a holistic approach for successful industrialisation. Read the full blog here: https://lnkd.in/exeUD4P6 hashtag #DataScienceTalent #DataScience #MVP #PredictiveAnalytics #MachineLearning