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Вміст надано David Yakobovitch. Весь вміст подкастів, включаючи епізоди, графіку та описи подкастів, завантажується та надається безпосередньо компанією David Yakobovitch або його партнером по платформі подкастів. Якщо ви вважаєте, що хтось використовує ваш захищений авторським правом твір без вашого дозволу, ви можете виконати процедуру, описану тут https://uk.player.fm/legal.
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How Data Scientists Transform the Financial Industry with Geoffrey Horrell from London Stock Exchange Group

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Manage episode 290293033 series 2512650
Вміст надано David Yakobovitch. Весь вміст подкастів, включаючи епізоди, графіку та описи подкастів, завантажується та надається безпосередньо компанією David Yakobovitch або його партнером по платформі подкастів. Якщо ви вважаєте, що хтось використовує ваш захищений авторським правом твір без вашого дозволу, ви можете виконати процедуру, описану тут https://uk.player.fm/legal.

How Data Scientists Transform the Financial Industry with Geoffrey Horrell from London Stock Exchange Group

Episode Show Notes:

  • Geoffrey Horrell was the Head of Refinitiv Labs, London and currently the Global Head of Innovation and Labs at the London Stock Exchange Group.
  • This episode unpacks key trends from Refinitiv’s new global research report ‘The Rise of the Data Scientist’. Data scientists are moving across teams and realizing (and scaling) new opportunities for AI across their firms - e.g. NLP. In addition, 75% of firms are now using Deep Learning while 17% of firms rely solely on unstructured data for AI/ML use-cases.
  • Data strategy is set to overtake tech strategy in importance. There is a further evolution of teams and talent across finance - more citizen data scientists in trading, investment teams etc.
  • Data scientists are going from supportive to strategic roles as investments in tech and talent put data strategy in the spotlight. COVID-19 is upsetting ML models where you need to recalibrate and calculate disruptive events.
  • NLP use-cases are going into production and impacting accuracy with more advancements increasing the need for ‘explainability’
  • Models, data and people are prepared for tomorrow’s unexpected shocks - COVID proved that models need re-calibrating / new data to calculate disruptive events (e.g. data enrichment).

Learn More about Refinitiv Labs:

About HumAIn Podcast

The HumAIn Podcast is a leading artificial intelligence podcast that explores the topics of AI, data science, future of work, and developer education for technologists. Whether you are an Executive, data scientist, software engineer, product manager, or student-in-training, HumAIn connects you with industry thought leaders on the technology trends that are relevant and practical. HumAIn is a leading data science podcast where frequently discussed topics include ai trends, ai for all, computer vision, natural language processing, machine learning, data science, and reskilling and upskilling for developers. Episodes focus on new technology, startups, and Human Centered AI in the Fourth Industrial Revolution. HumAIn is the channel to release new AI products, discuss technology trends, and augment human performance.


Advertising Inquiries: https://redcircle.com/brands
Privacy & Opt-Out: https://redcircle.com/privacy
  continue reading

119 епізодів

Artwork
iconПоширити
 
Manage episode 290293033 series 2512650
Вміст надано David Yakobovitch. Весь вміст подкастів, включаючи епізоди, графіку та описи подкастів, завантажується та надається безпосередньо компанією David Yakobovitch або його партнером по платформі подкастів. Якщо ви вважаєте, що хтось використовує ваш захищений авторським правом твір без вашого дозволу, ви можете виконати процедуру, описану тут https://uk.player.fm/legal.

How Data Scientists Transform the Financial Industry with Geoffrey Horrell from London Stock Exchange Group

Episode Show Notes:

  • Geoffrey Horrell was the Head of Refinitiv Labs, London and currently the Global Head of Innovation and Labs at the London Stock Exchange Group.
  • This episode unpacks key trends from Refinitiv’s new global research report ‘The Rise of the Data Scientist’. Data scientists are moving across teams and realizing (and scaling) new opportunities for AI across their firms - e.g. NLP. In addition, 75% of firms are now using Deep Learning while 17% of firms rely solely on unstructured data for AI/ML use-cases.
  • Data strategy is set to overtake tech strategy in importance. There is a further evolution of teams and talent across finance - more citizen data scientists in trading, investment teams etc.
  • Data scientists are going from supportive to strategic roles as investments in tech and talent put data strategy in the spotlight. COVID-19 is upsetting ML models where you need to recalibrate and calculate disruptive events.
  • NLP use-cases are going into production and impacting accuracy with more advancements increasing the need for ‘explainability’
  • Models, data and people are prepared for tomorrow’s unexpected shocks - COVID proved that models need re-calibrating / new data to calculate disruptive events (e.g. data enrichment).

Learn More about Refinitiv Labs:

About HumAIn Podcast

The HumAIn Podcast is a leading artificial intelligence podcast that explores the topics of AI, data science, future of work, and developer education for technologists. Whether you are an Executive, data scientist, software engineer, product manager, or student-in-training, HumAIn connects you with industry thought leaders on the technology trends that are relevant and practical. HumAIn is a leading data science podcast where frequently discussed topics include ai trends, ai for all, computer vision, natural language processing, machine learning, data science, and reskilling and upskilling for developers. Episodes focus on new technology, startups, and Human Centered AI in the Fourth Industrial Revolution. HumAIn is the channel to release new AI products, discuss technology trends, and augment human performance.


Advertising Inquiries: https://redcircle.com/brands
Privacy & Opt-Out: https://redcircle.com/privacy
  continue reading

119 епізодів

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