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    data scientists

    Explore " data scientists" with insightful episodes like "Ian Hook on Advancing Operational Excellence with AI and Knowledge Management - The Earley AI Podcast with Seth Earley - Episode #041", "Why AI is a double-edged sword in an election year – Angie Ma, co-founder, Faculty", "#09. Pioneering DataOps in the Age of AI with Audrey Smith", "AI Disruption Unleashed: Elevating Customer Journey with Mary Poppen" and "Building Machine Learning Apps" from podcasts like ""Earley AI Podcast", "UKTN | The Podcast", "Humans of AI", "Disruption / Interruption" and "The Cloudcast"" and more!

    Episodes (16)

    Ian Hook on Advancing Operational Excellence with AI and Knowledge Management - The Earley AI Podcast with Seth Earley - Episode #041

    Ian Hook on Advancing Operational Excellence with AI and Knowledge Management - The Earley AI Podcast with Seth Earley - Episode #041

    Ian Hook is an exemplary professional whose journey spanned from an early career as a blacksmith and preschool teacher to becoming a seasoned expert in knowledge management and artificial intelligence (AI) at Nevartis. His unorthodox path and hands-on experience have endowed him with a deep understanding of the intricacies of knowledge management and its pivotal role in leveraging generative AI tools efficiently and effectively within operational teams. Ian's significant contributions have led to remarkable operational efficiencies, including an $18 million savings for his company by centralizing market research resources.

    Key Takeaways:

    - Knowledge management and generative AI are integral to improving the speed and accuracy of issue detection and remediation in operational teams.

    - Understanding the lineage and flow of data is vital for data scientists to fulfill their responsibility effectively.

    - Ian Hook illustrates the considerable impact of having a centralized knowledge management platform on efficiency and cost savings within a corporate setting.

    - The importance of governance in the context of utilizing generative AI is highlighted to mitigate unreliable outcomes due to ungoverned data.

    - Knowledge graphs are presented as sophisticated tools that visualize expertise and the relationships between different domains of knowledge.

    - The episode explores the limitations of large language models and emphasizes the importance of human oversight to prevent inaccuracies.

    Quote of the Show:

    "In our quest to harness AI, we must remember that the texture of human knowledge and expertise is the bedrock upon which these systems must be built." - Ian Hook

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    Why AI is a double-edged sword in an election year – Angie Ma, co-founder, Faculty

    Why AI is a double-edged sword in an election year – Angie Ma, co-founder, Faculty

    Dr Angie Ma, co-founder of Faculty, explains how AI poses both a threat and an opportunity during an election year, explains why the company stopped doing political work post-Brexit referendum, and shares her top productivity tips for entrepreneurs.  

    Ma founded Faculty with Dr Marc Warner and Andy Brookes in 2014. The London-based firm began as a fellowship to help academics become commercial data scientists. It now provides software and consulting services to businesses, with clients including HSBC, Tide and John Lewis. Faculty was controversially paid by Vote Leave to provide services during the 2016 Brexit referendum, but said it stopped doing political work in 2019. Ma, who holds a PhD in physics and applied optics from UCL, previously served as Faculty’s chief operating officer and chief people officer. 

    #09. Pioneering DataOps in the Age of AI with Audrey Smith

    #09. Pioneering DataOps in the Age of AI with Audrey Smith

    In this episode of Humans of AI, host Sheikh Shuvo engages with Audrey Smith, the Chief Operating Officer of MLTwist. They delve into the fascinating world of automating data pipelines and the crucial role of DataOps in AI development.


    Key highlights of this episode include:

    • Audrey's Unique Perspective: Coming from a non-technical background, Audrey shares her unique viewpoint on machine learning and data operations, emphasizing the importance of diverse perspectives in tackling data bias.
    • The Evolution of DataOps: Audrey discusses her journey from law to leading operations at ML Twist, shedding light on the growing complexity and significance of DataOps in the AI industry.
    • Future Trends and Challenges: The conversation explores future trends in AI, such as the rise of synthetic data and the impact of regulatory frameworks like the EU AI Act on data management and ethical AI development.


    Join us for an enlightening discussion that uncovers the layers of DataOps and its integral role in shaping the AI landscape.

    AI Disruption Unleashed: Elevating Customer Journey with Mary Poppen

    AI Disruption Unleashed: Elevating Customer Journey with Mary Poppen

    Mary Poppen is the President and Managing Partner of the Employee Experience Division at HRIZONS, an HR Cloud Technology Company that helps HR professionals become more agile and effective leveraging HR cloud technology to meet the needs of their evolving workforce. In this episode Mary and KJ discuss the challenges of cross-functional alignment, the impact of poor customer experience, and the role of AI in personalizing customer experiences. 

     Key Takeaways:

    • How AI helps to deliver a seamless customer journey
    • Common mistakes in the handoff process between sales and delivery teams
    • How to foster collaboration between sales, delivery, and engineering teams
    • The importance of human oversight in training and validating AI systems
    • Why personalized playbooks deliver value-added experiences

    Quote of the Show (31:00):

    “There is always something that you can be doing to improve the customer experience. Any little action to big action makes a difference.” – Mary Poppen

     

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    Want PR that actually matters? Get 30 minutes of expert advice in a fast-paced, zero-nonsense session from Karla Jo Helms, a veteran Crisis PR and Anti-PR Strategist who knows how to tell your story in the best possible light and get the exposure you need to disrupt your industry. Click here to book your call: https://info.jotopr.com/free-anti-pr-eval

     

    Ways to connect with Mary Poppen:

    LinkedIn: https://www.linkedin.com/in/marypoppen/

    Twitter: Mary__Poppen

    Company Website: https://hrizons.com/
    Company LinkedIn: https://www.linkedin.com/company/hrizons/

     

    How to get more Disruption/Interruption: 

    Amazon Music - https://music.amazon.com/podcasts/eccda84d-4d5b-4c52-ba54-7fd8af3cbe87/disruption-interruption

    Apple Podcast - https://podcasts.apple.com/us/podcast/disruption-interruption/id1581985755

    Google Play - https://podcasts.google.com/feed/aHR0cHM6Ly93d3cub21ueWNvbnRlbnQuY29tL2QvcGxheWxpc3QvODE5NjRmY2EtYTQ5OC00NTAyLThjZjktYWI3YzAwMmRiZTM2LzNiZTZiNzJhLWEzODItNDhhNS04MDc5LWFmYTAwMTI2M2FiNi9kZDYzMGE4Mi04ZGI4LTQyMGUtOGNmYi1hZmEwMDEyNjNhZDkvcG9kY2FzdC5yc3M=

    Spotify - https://open.spotify.com/show/6yGSwcSp8J354awJkCmJlD

    Stitcher - https://www.stitcher.com/show/disruption-interruption

    See omnystudio.com/listener for privacy information.

    Building Machine Learning Apps

    Building Machine Learning Apps

    Tuhin Srivastava, (Co-Founder/CEO of @basetenco) talks about enabling Data Scientists to build better Machine Learning models and applications.   

    SHOW: 653

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    SHOW NOTES:

    Topic 1 - Welcome to the show. Tell us a little bit about your background and why you started Baseten.

    Topic 2 - Let’s start by talking about Data Scientists. Not an easy job. They build models and analyze data. But what typically happens after that? How do the models typically get seen and what needed to happen to make that possible?

    Topic 3 - What parts of those pain points does Baseten focus on? How does Baseten bring together low-code (or serverless) concepts with the complexity that Data Scientists need to deal with day-to-day?

    Topic 4 - What are some of the technologies running behind the scenes to make Baseten easy to use, or that are exposed to the Data Scientists? 

    Topic 5 - How does a Data Scientist typically get from “I have a useful model” to “I’m making this part of a useful application”? How does Baseten create building blocks to help them not have to be front-end or back-end engineers?

    Topic 6 - What are some of the types of use-cases or applications that your customers have been building?

    FEEDBACK?

    Using Data to Transform the Fan Experience in Football and Basketball: Insights from the Indianapolis Colts and Orlando Magic

    Using Data to Transform the Fan Experience in Football and Basketball: Insights from the Indianapolis Colts and Orlando Magic

    There’s nothing like watching your favorite sports team live and in-person. The roar of the crowd. The smell of the concession stand. The suspense of the game clock winding down to its final seconds. But would you have guessed that behind every ticket purchase, box of cracker jacks, and Jumbotron moment, there are teams of data professionals working to make your fan experience even more seamless and engaging?

    Joining Cindi today to discuss the data and analytics powering our favorite sports events are Jay Riola, the SVP of Strategy & Innovation for the Orlando Magic, and Charlie Shin, the VP of Data Strategy and Analytics for the Indianapolis Colts. 

    With perspectives from both the NBA and the NFL, Jay and Charlie explore the evolution of mobile ticketing, challenges with identity management, the importance of building fan trust, and the most surprising insights they’ve ever discovered within their data. 

    Key Takeaways

    • Customers expect more for their data: When customers share their data, they expect something in return. Respecting their privacy and keeping their data safe are the bare minimum. They expect real, tangible value. As a business, your responsibility is to use this data to enhance their experience. Whether that be through custom offers, more relevant content recommendations, or more seamless purchasing experiences, what matters most is that you’re delivering on the expectation of value.
    • Collecting data is one thing, putting it together is another: While technologies like mobile payments and social media have made it easier for businesses to collect data, they’ve also added complexity to the process of building accurate customer profiles. To truly understand the person on the other side of the screen, you must have the right skills and infrastructure to bring all that data together. 
    • Great data scientists need to be a triple threat: It is no longer enough to be very good at the technical components of your job and “so-so” at translating insights into actionable recommendations for business stakeholders. The best data scientists have technical chops, a deep understanding of how their business works, and the storytelling skills to turn insights into influence.

    Key Quotes

    Charlie:

    • “In sports, they started off by focusing on the product, which is the on-field performance, our players. So a lot of the emphasis was using datas to optimize their investments, enhance their quality of on-field performance. But as the competition grew… now we've seen a shift where the focus is more on the customers and their experience in addition to the on-field quality.”
    • “Identity management is a key topic in sports at this point… We have a variety of different data sources, whether it's ticketing, merchandising, digital engagement, or website or apps, there's a lot of information that's coming through. And then we're trying to figure out how do we tie all this together so that we have that clear understanding of that single view of our customers across these touchpoints. And I don't think this is just a sports industry challenge, right? I think it's a challenge across all industries that manage consumer information.”

    Jay:

    • “We were a pretty early adopter of variable ticket pricing and thinking about the value from a ticket perspective of our games differently based on the team that we were playing, the time of the year, whether it was early in the season versus later in the season, obviously weekday versus weekend, but just recognizing that the marketplace values these games differently and so should we... Then it became, how do we dynamically price our tickets? How are we changing pricing as we approach games to reflect the demand situation that we have or if an opponent is performing better or worse than we expected, and we can raise or lower pricing. I think where data is really helping guide us… is product development and thinking about ticketing in new and kind of transformational ways.”
    • “We have seen significant growth in ticketing revenue and improvement in retention of fans, as we've introduced this more sophisticated way of thinking about pricing and sales to our business. And I would venture to guess that most teams that have implemented this are seeing returns as well in terms of revenue growth and also total ticket sold.”
    • “We are fortunate to work in an industry where fans are more willing to share their data with us… But I do think that the same expectations do come along, which is I'm giving you something and in return, there's an expectation, obviously that you will protect my data and store it safely… but also that now you're going to enrich my experience with you somehow… I think it's kind of shifting responsibility to companies to be far more active in how they think about not just security and data management, but returning value on that expectation that will come from your fans and your consumers.”

    About Charlie

    Charlie Shin is a highly analytical customer strategy and marketing executive with global and domestic experience in data analytics, strategic planning, project management, customer segmentation, customer relationship management, and KPI management. He excels at guiding enterprise data strategy, CRM initiatives, and organization-wide marketing technology infrastructure.

    Prior to joining the Colts, Charlie was a VP of Strategy & Analytics at MLS for past 15 years where he developed the foundation and enhanced league-wide data strategy, analytic capabilities and CRM technology infrastructure. He also worked as a senior business consultant at Samsung OpenTide and PwC Consulting for over six years managing various projects related to customer strategy, CRM strategy, performance marketing, customer segmentation and new business model development. In addition, he currently serves as an adjunct faculty at NYU and Columbia University.

    Charlie holds a BA in business administration from Yonsei University and an MS in sports business from New York University.

    About Jay

    Jay Riola is entering his 16th season with the Orlando Magic. He was promoted to senior vice president of strategy & innovation in July 2019. Riola oversees the Magic’s business strategy and innovation efforts including data engineering, strategy and analytics, mobile strategy, CRM, digital marketing and marketing technology, as well as other strategic initiatives and special projects.

    Riola started with the Magic as an intern in 2006 and worked as part of the Magic’s internal team overseeing the design and construction of the Amway Center, which opened in 2010. Since 2010, he has worked in several roles to grow the Magic’s data and analytics program from a small, startup effort into a department that is regarded by sports industry professionals as a best-in-class team. Riola has also helped lead the Magic’s mobile strategy and digital technology efforts, including advancement of the team’s mobile app and development of new and innovative digital ticketing solutions. In 2016, he helped lead the process to bring the Orlando Magic’s G-League team, the Lakeland Magic, to Lakeland, Florida, negotiating the deal with the City of Lakeland and the RP Funding Center.

    In addition to his role with the Magic, Riola is an adjunct instructor with the DeVos Sport Business Management Graduate Program at the University of Central Florida, where he teaches a sport business analytics course. He is active in the broader sports business industry serving on several boards and advisory committees, including currently serving as chair for UCF’s DeVos Sports Business Management Program’s Advisory Board, Baylor University’s Center for Sports Strategy and Sales (S3), KORE Software’s Customer Advisory Board, the Greater Orlando Sports Commission’s Human Rights Committee for its 2026 FIFA World Cup Candidate City Bid and the NBA’s Team Innovation Advisory Council (TIAC). Riola also serves on the board of Sports2Change, a nonprofit organization he founded that encourages volunteerism among youth student-athletes in Central Florida.

    Riola received his bachelor's degree in business administration with concentrations in finance and marketing from Trinity University in San Antonio, Texas in 2006, where he played on the men’s basketball team. He received his MBA from the University of Florida in 2011. Riola currently resides in Orlando’s College Park neighborhood with his wife, Julia. They have a daughter, Madeline, and a son, Mason.

    --

    The Data Chief is presented by our friends at ThoughtSpot. Searching through your company’s data for insights doesn’t have to be complicated. With ThoughtSpot, anyone in your organization can easily answer their own data questions, find the facts, and make better, faster decisions. Learn more at thoughtspot.com

    The Quant Model Problem

    The Quant Model Problem

    Often we focus on models so much that we forget they aren't perfect. There is a strong need for a validation team to check the work of model development. However this leads to disagreements between teams. There are many factors that play into this which include technical limitations, limitations of employee education and experience, usage limitations, and ego. Quants make their money based on their intelligence. It is hard to admit you either made a mistake or do have the knowledge required to model a specific problem. Add on the requirement to do additional work and potential negative impacts to your promotions and bonuses and we end up in a hostile environment. These limitations are risks in themselves and should be viewed as part of model risk management's analysis.

    If you found this video helpful, please consider buying me a coffee through Ko-Fi.

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    [Bite] Why Data Science projects fail

    [Bite] Why Data Science projects fail

    Data Science in a commercial setting should be a no-brainer, right? Firstly, data is becoming ubiquitous, with gigabytes being generated and collected every second. And secondly, there are new and more powerful data science tools and algorithms being developed and published every week. Surely just bringing the two together will deliver success...

    In this episode, we explore why so many Data Science projects fail to live up to their initial potential. In a recent Gartner report, it is anticipated that 85% of Data Science projects will fail to deliver the value they should due to "bias in data, algorithms or the teams responsible for managing them". There are many reasons why data science projects stutter even aside from the data, the algorithms and the people.
    We discuss six key technical reasons why Data Science projects typically don't succeed based on our experience and one big non-technical reason!

    And being 'on the air' for a year now we'd like to give a big Thank You to all our brilliant guests and listeners  - we really could not have done this without you! It's been great getting feedback and comments on episodes. Do get in touch jeremy@datacafe.uk or jason@datacafe.uk if you would like to tell us your experiences of successful or unsuccessful data science projects and share your ideas for future episodes.

    Further Reading and Resources



    Some links above may require payment or login. We are not endorsing them or receiving any payment for mentioning them. They are provided as is. Often free versions of papers are available and we would encourage you to investigate.

    Recording date: 18 June 2021

    Intro music by Music 4 Video Library (Patreon supporter)

    Thanks for joining us in the DataCafé. You can follow us on twitter @DataCafePodcast and feel free to contact us about anything you've heard here or think would be an interesting topic in the future.

    Why every company need to hire Data scientist

    Why every company need to hire Data scientist

    Listen to this podcast to know why companies hire Data Scientists. 


    Data Scientist are analytical experts who utilize their skills in both technology and social science to find trends and manage data.

    Data Scientist is someone who makes value out of data. Data scientist duties typically include creating various machine learning-based tools or processes within the company, such as recommendation engines or automated lead scoring systems. People within this role should also be able to perform statistical analysis.

    We from BEPEC are ready to help you and make you shift your career at any cost

    Book a free call consultation & Get customized Career Transition Roadmap: https://www.bepec.in/registration-form

    Check our Instagram page:  https://www.instagram.com/bepec_solutions/

    Drive CX and Revenue with NLP in marketing and ecommerce, E26

    Drive CX and Revenue with NLP in marketing and ecommerce, E26
    • 01:25 - Do NLP models need someone that is not completely monolingual?
    • 05:20 - Types of NLP  in marketing and/or e-commerce.
    • 11:30 - Challenges in the e-commerce space: Behavioural data gathered by cookies has disappeared.
    • 16:00 - Every 40 seconds, our attention breaks. Is that fact taken into account in NLP modeling for personalization?
    • 18:20 - Models like GPT-3 open a whole new commercialization avenue in the marketing world, specifically for content creation. Impact of the wave.
    • 21:50 - Is it fair to use an AI model for IP and content in such a way you influence millions of users on a website at once?
    • 30:45 - Explainable models, debugging and how models could function.
    • 37:00 - Provocative contexts for data scientists nowadays.
    • 41:00 - Future of NLP.

    Episode references:

    Matt Godbolt: Software Testing, Performance Tuning, and Code Handoff for Data Scientists

    Matt Godbolt: Software Testing, Performance Tuning, and Code Handoff for Data Scientists

    Data scientists and ML engineers write a lot of code: building data pipelines, wiring up models, and sometimes translating concepts from research papers into algorithms.  

    Once in a while, that code runs into performance problems.  These can be painful to debug when you don't come from a formal software development background.  That's why Formulatedby's Senior Content Advisor Q McCallum rang up Matt Godbolt to learn the deep details of software testing, tracing performance bugs, working with data at scale, and how data scientists can work with developers to prepare their code for a production handoff.

    Matt Godbolt has more than 30 years' experience writing code.  He's spent most of that time working in the performance-focused environments of console video games, high-frequency trading (HFT), and algorithmic trading.  Matt is the creator of the Compiler Explorer website, and also co-host of the Two's Complement podcast.

    (Note from Q: My audio is a little choppy, but Matt's is perfect.  And you're here to hear him, anyway...)

     

    Matt and Q mentioned a few links during their talk:

    Why data scientists need to “study up”. ft. Chelsea Barabas

    Why data scientists need to “study up”.  ft. Chelsea Barabas

    Check out Chelsea’s work on Twitter and Medium.

    Created by SOUR, this podcast is part of the studio's "Future of X,Y,Z" research, where the collaborative discussion outcomes serve as the base for the futuristic concepts built in line with the studio's mission of solving urban, social and environmental problems through intelligent designs.

    Find out what today’s guest and former guests are up to by following What’s Wrong With on Instagram and on Twitter

    Make sure to visit our website - podcast.whatswrongwith.xyz - and subscribe to the show on Apple Podcasts, Spotify, or Google Podcasts so you never miss an episode. 

    If you found value in this show, we would appreciate it if you could head over to iTunes to rate and leave a review – or you can simply tell your friends about the show!

    Don’t forget to join us next week for another episode. Thank you for listening!

    Cybersecurity and the C-Suite

    Cybersecurity and the C-Suite

    During Cyber Florida Conference 2019, a panel of respected cybersecurity experts gathered to share their insights on how cybersecurity impacts the C-level professional, changes in accountability and business models, and what it means to build a cyber-strong workforce. The panel was moderated by Mark Clancy, Chief Information Security Officer (CISO) for Sprint. The esteemed guests on the panel were Diane Janosek, NSA Commandant of the National Cryptologic School; Andy Zolper, SVP, CISO, and Head of Technology Infrastructure for Raymond James Financial; and Terry Roberts, Founder and President of WhiteHawk, Inc.

    C-level professionals have been a driving force in developing business and securing infrastructures. Recent breaches resulting in CEO firings and similar repercussions are impacting the way many C-level leaders are engaging with technology and their workforce’s cyber culture. Cyber Florida took the opportunity at the conference to help both the C-level professionals and stakeholders who are part of their decision-making process with a discussion titled “Cybersecurity and the C-Suite.” The panelists discussed why it is vital for C-level executives to embrace cybersecurity education and innovation. The experts spoke to what factors C-level leadership face in their organization and workforce in relation to security, networking, and data fundamentals. A large portion of the conversation focused on identifying what it takes to onboard a workforce in this computer-centric modern life (with the phrase “cyberize” being coined to discuss the process), and understanding the crossover that is occurring because of the inter-connectivity of roles and risks.

    Panelists discussed case studies and resources, such as cyber executive programs, where C-level professionals can:

    • learn the basics of cybersecurity,
    • embrace accountability at the C-level,
    • identify the risks and opportunities of current infrastructure and future tech,
    • learn how certain business models are changing as a response to technology,
    • establish pillars for a robust cyber culture,
    • understand independent cyber risk ratings as a commodity,
    • develop a cyber-strong workforce, and
    • create a constructive response plan to cybersecurity attacks and cybercrimes.

    This is a two-part edition with the second part discussing the personnel gap in cybersecurity and what can be done about it. You can find parts 1 and 2, as well as other episodes of No Password Required podcast, on our website at https://cyberflorida.org/podcast/. This special edition was recorded at Cyber Florida Conference 2019 in Tampa, Florida. Learn about upcoming Cyber Florida events, including the annual conference, at cyberflorida.org or follow us on social media.

    TIME STAMPS

    00:42 Who is Diane Janosek, Cybersecurity Expert, Cyber Security Woman of the Year

    02:03 Who is Andy Zolper, CISO at Raymond James Financial

    02:45 Who is Terry Roberts, Cybersecurity Exchange

    03:58 How to Communicate Cybersecurity to Leadership

    07:25 C-Level Accountability, Cyber Risk Ratings are a Commodity, Cyber Executive Program

    09:53 Hiring a Cybersecurity Workforce and Training a Cybersecurity Culture

    15:55 Innovation in Education for Cybersecurity and Cyber Risk Training

    18:50 Identifying, Leading and Managing Critical Skills

    21:54 Cyberize Your Team, Workforce Crossovers, and Cyber Defense Ecosystem

    26:07 Business Interruption and Constructive Actions to Address Cyber Crimes

    27:35 Cyber Executive Programs and Case Management

    The Death of Data Viz, Cross-Cultural AI, and AI Auditing

    The Death of Data Viz, Cross-Cultural AI, and AI Auditing

    In our second-to-last episode of the season, Triveni and Will explore the data world’s shifting attitude toward standalone data visualizations (are they dying? Who are they for?), how to respond to global AI practices (what are global AI standards? How do different countries vary in their AI approaches?), and the feasibility of an AI audit. We’ll also see how Spark fits into the infrastructure of our data science systems.

    Be sure to subscribe to our weekly newsletter to get this podcast & a host of new and exciting data-happenings in your inbox! 

    Learn more about the articles referenced in this episode below:

    Standalone Data Visualization is Dead...and I Couldn’t Be More Excited by Matthew Miller (Biztory) 

    IDC: Asia-Pacific spending on AI systems will reach $.5 billion this year, up 80% from 2018 by Catherine Shu (TechCrunch) 

    High-Stakes AI Decisions Need to Be Automatically Audited by Oren Etzioni and Michael Li (WIRED)