#222 Hiring And Retention In 2023: Positioning Your Organisation with the Right Audience, with Felipe Flores

Data Futurology - Leadership And Strategy in Artificial Intelligence, Machine Learning, Data Science

Player FM - Internet Radio Done Right

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تمت الإضافة منذ قبل seven عام

المحتوى المقدم من Felipe Flores. يتم تحميل جميع محتويات البودكاست بما في ذلك الحلقات والرسومات وأوصاف البودكاست وتقديمها مباشرة بواسطة Felipe Flores أو شريك منصة البودكاست الخاص بهم. إذا كنت تعتقد أن شخصًا ما يستخدم عملك المحمي بحقوق الطبع والنشر دون إذنك، فيمكنك اتباع العملية الموضحة هنا https://ar.player.fm/legal.

TechSurge: Deep Tech VC Podcast

1
Understanding the Elegant Math Behind Modern Machine Learning 1:14:43

منذ 13 يومًا1:14:43

التشغيل لاحقا

قوائم

إعجاب

احب

1:14:43

Artificial intelligence is evolving at an unprecedented pace—what does that mean for the future of technology, venture capital, business, and even our understanding of ourselves? Award-winning journalist and writer Anil Ananthaswamy joins us for our latest episode to discuss his latest book Why Machines Learn: The Elegant Math Behind Modern AI . Anil helps us explore the journey and many breakthroughs that have propelled machine learning from simple perceptrons to the sophisticated algorithms shaping today’s AI revolution, powering GPT and other models. The discussion aims to demystify some of the underlying mathematical concepts that power modern machine learning, to help everyone grasp this technology impacting our lives–even if your last math class was in high school. Anil walks us through the power of scaling laws, the shift from training to inference optimization, and the debate among AI’s pioneers about the road to AGI—should we be concerned, or are we still missing key pieces of the puzzle? The conversation also delves into AI’s philosophical implications—could understanding how machines learn help us better understand ourselves? And what challenges remain before AI systems can truly operate with agency? If you enjoy this episode, please subscribe and leave us a review on your favorite podcast platform. Sign up for our newsletter at techsurgepodcast.com for exclusive insights and updates on upcoming TechSurge Live Summits. Links: Read Why Machines Learn, Anil’s latest book on the math behind AI https://www.amazon.com/Why-Machines-Learn-Elegant-Behind/dp/0593185749 Learn more about Anil Ananthaswamy’s work and writing https://anilananthaswamy.com/ Watch Anil Ananthaswamy’s TED Talk on AI and intelligence https://www.ted.com/speakers/anil_ananthaswamy Discover the MIT Knight Science Journalism Fellowship that shaped Anil’s AI research https://ksj.mit.edu/ Understand the Perceptron, the foundation of neural networks https://en.wikipedia.org/wiki/Perceptron Read about the Perceptron Convergence Theorem and its significance https://www.nature.com/articles/323533a0…

منذ عامين 13:40

MP3•منزل الحلقة

Organisations face multiple challenges when it comes to building teams in 2023. On the one hand, there is a skill shortage in just about every field of data science and analytics. Finding and attracting the best people to the organisation can be difficult.

On the other hand, there is mobility between jobs unlike anything we’ve seen before. The “Great Resignation” is still a major trend sweeping across Australia, and employees will be more than willing to move on if they don’t feel like they’re getting what they need from their jobs.

In this episode with Data Futurology podcast host Felipe Flores (a Chief Data, Analytics & Technology Officer himself), he explores both sides of this particular coin. In the first half, Felipe shares key insights and tips on how to recruit the best talent, including mistakes that he’s made in hiring and how he now looks at the interview and hiring process.

The second half of the podcast is dedicated to providing tips for retention. Contrary to the popular view, it’s not always a matter of remuneration. Indeed, studies consistently show that this is far less important to many employees than things such as the opportunity to build their skills or engage more deeply with their organisation. As Felipe says “People might want to become a product owner, or a strategic person that interfaces with the business and helps them to contextualise the results to the organisation.”

Tune into the podcast for these insights, and many more.

Thank you to our sponsor, Talent Insights Group!

Join us in Sydney for OpsWorld: https://www.datafuturology.com/opsworld

Join our Slack Community: https://join.slack.com/t/datafuturologycircle/shared_invite/zt-z19cq4eq-ET6O49o2uySgvQWjM6a5ng

What We Discussed:

00:00 Welcome to Data Futurology

0:29 What to expect from our upcoming event on operationalizing security for business value, impact and scale, at the Sofitel Wentworth in Sydney on March 14 and 15.

2:20 What makes hiring so challenging.

2:50 Three tips for hiring. Tip #1: Attitude.

3:40 Three tips for hiring. Tip #2: Transparency and openness.

5:53 Three tips for hiring. Tip #2: Be impressed with one technical thing in one technical area.

7:54 Why retention is important, and what is being done to improve it?

9:54 Three tips for retention. Tip #1: Provide formal training.

10:24 Three tips for retention. Tip #2: Give employees exposure to new work/projects.

11:31 Three tips for retention. Tip #3: Provide on-the-job training

Quotes:

Even having technical tests doesn't really show the full depth and capability of a person. It’s very easy to get it wrong.
When I was more junior in my hiring career, I would test people in the interview. We always had a technical test, and then an interview where we were going through the code, they were just wrong. This is terrible, but when I was junior, I would sometimes tell people “Hey, that’s wrong.” The idea was that if someone responded “oh, yeah, let’s discuss that” then those were the people we wanted to hire. That’s not a very effective way to do it.
I don’t look for somebody to impress me with general data engineering or data science skills. Rather, it could be something like the way they use one algorithm in a particular way.
You want one technical thing that people do well because it shows passion, commitment, and that they really care.

268 حلقات

#Data Science #Science #Felipe Flores #Tech

#222 Hiring And Retention In 2023: Positioning Your Organisation with the Right Audience, with Felipe Flores

Data Futurology - Leadership And Strategy in Artificial Intelligence, Machine Learning, Data Science

349 subscribers

published منذ عامين

MP3•منزل الحلقة

Tune into the podcast for these insights, and many more.

Thank you to our sponsor, Talent Insights Group!

Join us in Sydney for OpsWorld: https://www.datafuturology.com/opsworld

Join our Slack Community: https://join.slack.com/t/datafuturologycircle/shared_invite/zt-z19cq4eq-ET6O49o2uySgvQWjM6a5ng

What We Discussed:

00:00 Welcome to Data Futurology

0:29 What to expect from our upcoming event on operationalizing security for business value, impact and scale, at the Sofitel Wentworth in Sydney on March 14 and 15.

2:20 What makes hiring so challenging.

2:50 Three tips for hiring. Tip #1: Attitude.

3:40 Three tips for hiring. Tip #2: Transparency and openness.

5:53 Three tips for hiring. Tip #2: Be impressed with one technical thing in one technical area.

7:54 Why retention is important, and what is being done to improve it?

9:54 Three tips for retention. Tip #1: Provide formal training.

10:24 Three tips for retention. Tip #2: Give employees exposure to new work/projects.

11:31 Three tips for retention. Tip #3: Provide on-the-job training

Quotes:

Even having technical tests doesn't really show the full depth and capability of a person. It’s very easy to get it wrong.
When I was more junior in my hiring career, I would test people in the interview. We always had a technical test, and then an interview where we were going through the code, they were just wrong. This is terrible, but when I was junior, I would sometimes tell people “Hey, that’s wrong.” The idea was that if someone responded “oh, yeah, let’s discuss that” then those were the people we wanted to hire. That’s not a very effective way to do it.
I don’t look for somebody to impress me with general data engineering or data science skills. Rather, it could be something like the way they use one algorithm in a particular way.
You want one technical thing that people do well because it shows passion, commitment, and that they really care.

268 حلقات

#Data Science #Science #Felipe Flores #Tech

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منذ 2 years38:52

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43:13

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منذ 2 years27:08

27:08

In this episode, we explore an engaging talk given by Niall Keating, General Manager for Technology Data Platforms at Sportsbet, during his recent appearance at the Data Engineering Summit in Melbourne. Niall generously imparts invaluable insights on the journey of cultivating a data culture that yields long-lasting business impact. Throughout the conversation, Niall showcases tangible examples of how Sportsbet has effectively utilised data and technology to drive innovation and elevate customer experiences. Sportsbet, Australia's largest online bookmaker, faces unique challenges due to the dynamic nature of their product, where prices constantly change. To overcome these challenges, Sportsbet has invested significantly in technology and data infrastructure. One use case Niall highlights is their adoption of machine learning, with over 20 models currently in production. These models are employed to extract actionable insights, enabling Sportsbet to make data-driven decisions and enhance their offerings. Niall emphasises the importance of establishing a solid foundation in data culture and leveraging data for decision-making and financial reporting. He provides a specific use case of how Sportsbet utilises quantitative analytics to calculate probabilities and set prices for their core product. By harnessing data and analytics, Sportsbet optimises generosity, personalised experiences, and aims to provide the best value to their customers. Another use case Niall discusses is the application of data in safer gambling. Sportsbet is committed to making gambling safer, and they leverage data to identify potentially risky behaviours and intervene when necessary. Niall highlights the journey Sportsbet has undertaken over the past five years in building effective data products to promote safer gambling practices. When it comes to sustainability in data, Niall shares three educational stories that provide valuable insights. In one use case, he emphasises the importance of avoiding quick wins and taking an iterative approach aligned with strategic goals. He discusses the challenges involved in transitioning from human to AI automated decisions and the need to bridge the gap effectively. Lastly, Niall shares a use case centred around Sportsbet's product journey in safer gambling. He highlights the time and collaboration required to build effective data products that prioritise customer safety. This use case demonstrates the impact that data-driven approaches can have in creating a safer gambling environment. By adopting a long-term perspective and focusing on values such as safer gambling and customer-centricity, Sportsbet sets an example of how data culture can drive innovation and create positive outcomes. Enjoy the show! Thank you to our sponsor, Talent Insights Group ! Join us for our next events Advancing AI and Data Engineering Sydney (5-7 September): https://www.datafuturology.com/events Join our Slack Community: https://join.slack.com/t/datafuturologycircle/shared_invite/zt-z19cq4eq-ET6O49o2uySgvQWjM6a5ng Topics Discussed: 02:39. Introduction to Niall Keating and his background in software engineering. 04:08 Overview of Sportsbet as Australia's largest online bookmaker, serving one million active customers. 05:04 Investments in technology and data infrastructure, with a focus on machine learning and the impact of over 20 models in production. 07:06 The importance of getting the basics right in data-driven decision-making, financial reporting, and core product development. 09:14 The journey towards sustainability, including the focus on personalization, safer gambling, and aligning products with the company's vision and mission. 15:37 The challenges and lessons learned in evolving the data platform, including the adoption of lake house architecture and partnerships with AWS and Databricks. 22:04 The importance of building data products over time, collaboration between data science and analytics teams…

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منذ 2 years52:34

52:34

This week on the Data Futurology podcast, we have an in-depth conversation with Conor O’Neill, the Head of Data Science at Compare The Market exploring his career journey and current role leveraging data and innovating with machine learning. When O’Neill landed at Compare The Market, he quickly found himself in a senior data role within an organisation that needed to both transform and mature its approach to data. On the podcast, O’Neill walks through the various stages of transformation, and getting the rest of the organisation aligned with that vision. He also shares some use cases that Compare The Market is effectively leveraging data for, as well as how they have been building ML products. He explains how he involves data scientists in this process and offers advice on building ML as a product when it comes to planning, delivery and infrastructure. Finally, O’Neill shares some thoughts on the difference between a data scientist’s role and that of a senior manager, and how this shifts the perspective and how a data professional will look at projects. He then rounds out the conversation with some thoughts about where data science is heading as a profession. For anyone interested in data science, O’Neill’s unconventional journey into and through the profession is both interesting and inspiring. Enjoy the show! Connect with Conor: https://www.linkedin.com/in/conoroneill1/ Thank you to our sponsor, Talent Insights Group ! Join us for our next events Advancing AI and Data Engineering Sydney (5-7 September): https://www.datafuturology.com/events Join our Slack Community: https://join.slack.com/t/datafuturologycircle/shared_invite/zt-z19cq4eq-ET6O49o2uySgvQWjM6a5ng What we discussed 2:26: Felipe introduces Conor O’Neill. 3:23: O’Neill shares his journey from astrophysics to data science. 6:49: In astrophysics, the data sets that scientists work on are massive. O’Neill shares some insights about how he managed data in that role. 8:40: O’Neill shares his journey at Compare The Market so far. 12:04: O’Neill shares some information about a current data project that he and his team are working on. 18:08: Compare The Market had to do significant foundational work in transformation. O’Neill shares insights into that process. 21:18: O’Neill shares his experience in getting the Compare The Market organisation aligned behind their data vision. 25:12: O’Neill explains the value of having data scientists involved at the earliest stages of transformation design. 28:44: O’Neill describes his experience in moving from a data scientist role to heading a team, and the differences between these roles. 32:56: O’Neill explains some of the thinking that goes into reusing data projects, as well as how they decide the projects to not follow through. 34:04: Getting a model in front of the end users and driving adoption is a critical step – O’Neill explains how he has approached it for Compare The Market. 37:54: O’Neill overviews the various consumers of the work done by the data team, and how the data team needs to think about each of them. 40:51: Tips and guidance for creating ML as a product to be consumed internally 45:48: O’Neill shares some thoughts on how the data science industry is evolving. Key Quotes “We’ve been on a transformational journey now for a little over a year, and that’s been really good. We’ve been migrating off our legacy on-prem stack to Databricks. We’ve also been focused on getting the right people, and then also establishing a process, because if you just change the tool, you haven't fixed the issues, typically.” “You don't want your control group to be too large and you then miss opportunities. But you also don't want it to be so small that you don't get sufficient data. That's where the algorithm behind our recommendation system controls that, to optimise according to our confidence that we are or are not exceeding the required threshold, and adjust the weighting of the control group accordingly.”…

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#235: Maximising the productivity of the data-led enterprise with UNSW, EG Australia and Compare the Market 39:52

منذ 2 years39:52

39:52

This week we bring you a special episode of the Data Futurology podcast, featuring the keynote panel from our OpsWorld conference earlier this year featuring guests at different levels of data maturity. They shared their stories of the journey to enabling and unlocking the true value of data self-service.. The panel featured Kate Carruthers, Chief Data & Insights Officer, UNSW Sydney. She shared the university's experience, which has had a mature data environment for several years. At the other end of the table was Conor O'Neill, Head of Data Science, Compare The Market. He represented an organisation that is rapidly addressing a lack of data maturity across the organisation. The third person on the panel was Arvee Manaog, Head of Enterprise Systems, Data & Information Management, and Integration, EG Australia. She shared insights on how to effectively get organisation-wide buy-in, and then effectively educate all stakeholders on how to effectively use self-service. The panel was wide-ranging, starting off with a discussion around best practices in data self-service, before moving on to an in-depth summary of how to effectively approach self-service from each level of data maturity. There was also a robust Q & A session at the end of the panel. Through the robust audience questions, the panellists discussed strategies for ensuring data trustworthiness in self-service. They also discussed how ROI is best measured with self-service data practices. Businesses of all sizes that want to maximise data value should look at effective self-service approaches. This panel provides invaluable insights into both getting started and continuing to innovate once the data environment has been fully modernised and transformed. Enjoy the show! Thank you to our sponsor, Talent Insights Group ! Join us for our next events Advancing AI and Data Engineering Sydney (5-7 September): https://www.datafuturology.com/events Join our Slack Community: https://join.slack.com/t/datafuturologycircle/shared_invite/zt-z19cq4eq-ET6O49o2uySgvQWjM6a5ng What we discussed: 2:07: Felipe introduces the three panelists. 3:19: Carruthers explains UNSW’s perspective around best practices in data self-service. 6:23: Manaog explains the challenges of secure self-service in EG Australia. 10:38: Manaog explains the initial steps EG Australia took to get started on the data self-service journey. 14:40: O’Neill describes some self-service approaches he's seen work well. 19:50: Carruthers describes how UNSW has kept engagement with DevOps-created dashboards and models high across the organisation. 22:50: The panel takes audience questions, with the first being “How do we influence and motivate data silo owners to share for indirect enterprise outcomes?” 27:07: How can a mature data organisation bring together data literacy and digital literacy across users? 28:11: For a less mature data organisation, how can data leads ensure data trustworthiness in self-service? 30:14: There are trade-offs involved in self-service models. How can those be managed in the pursuit of a self-service culture? 35:38: What are the most effective techniques for measuring ROI with self-service data practices? Key quotes: Manaog: “We’re using DataIQ. And it actually helps because it's easier for users. I got a good adoption rate for that because it’s possible to do drag and drop, there are recipes and users don't need to code. They can easily do their analysis, create their workflows and then come to the hub and say, can you productionise this?” O’Neill: “In one model, we're doing a hub and spoke approach, where we have champions placed within the business units. We are working with those champions to ensure that we understand how they're using the report. It’s not just what they want to see. But in practice, what are they doing with it?”…

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Data Futurology - Leadership And Strategy in Artificial Intelligence, Machine Learning, Data Science « » #222 Hiring And Retention In 2023: Positioning Your Organisation with the Right Audience, with Felipe Flores

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مشابه لـData Futurology - Leadership And Strategy in Artificial Intelligence, Machine Learning, Data Science

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#222 Hiring And Retention In 2023: Positioning Your Organisation with the Right Audience, with Felipe Flores