Data Futurology - Data Science, ML and AI From Top Industry Leaders show

Data Futurology - Data Science, ML and AI From Top Industry Leaders

Summary: Data Futurology is data science from a human lens. In Data Futurology, experienced Data Science Leaders from around the world tell us their stories, challenges and the lessons learned throughout their career. We also ask them: - What makes a great data scientist? What skills are required? - How to become a great data science leader? - How should I grow and get the most out of my team? - What is a good data strategy? and how do I best implement it? - What are interesting applications of ML/AI that I should be considering in my industry? To find out more visit www.datafuturology.com

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Podcasts:

 #73 Powering Change using AI with Alex Ermolaev – AI Leader | File Type: audio/mpeg | Duration: 3207

Alex Ermolaev has been involved in the software industry for 20 years, including AI-specific experience at Bell Labs, Microsoft, several startups and now Nvidia. He is currently a leading AI software developer and works with groundbreaking companies that are implementing incredible AI solutions across several domains. In this episode, Alex describes how he started in the data space. Early in his career, he got a chance to work on a lot of data and software products. Enjoy the show! We speak about: [01:50] How Alex started in the data space [04:55] Alex’s professional background [10:30] Working for the finance team at Microsoft [14:55] Business development skills [18:50] Challenges working with startups [22:10] Working at Nvidia [26:20] Successful and unsuccessful AI patterns [30:00] AI and collecting data [35:15] How to tackle data problems using AI [40:30] Exciting uses for AI [43:15] The execution of new AI programs [49:00] What Alex is most proud of [50:20] Be patient and invest in your knowledge Resources: Alex’s LinkedIn: https://www.linkedin.com/in/alexermolaev Quotes: “The best way to develop knowledge in any area is to experience it.” “It is easier to sit in an office and assume the world works in a certain way.” “Don’t be in startups because it’s cool, try and find a path that meets your own needs.” “Working with startups is a lot of broader outreach and helping the community understand what is possible.” Thank you to our sponsors: Fyrebox - Make Your Own Quiz! RMIT Online Master of Data Science Strategy and Leadership Gain the advanced strategic, leadership and data science capabilities required to influence executive leadership teams and deliver organisation-wide solutions. Visit online.rmit.edu.au for more information And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show! --- Send in a voice message: https://anchor.fm/datafuturology/message

 #72 Focusing on Simplification to Solve Data Science Roadblocks with Evan Shellshear – Head of Analytics | File Type: audio/mpeg | Duration: 3236

Evan Shellshear has been an entrepreneur for more than a decade, and throughout that time he has always loved getting his hands dirty with building products from scratch and then commercializing them. Evan has a passion for innovation and not just from a managerial perspective but also from a doing perspective. He has a Ph.D. in Game Theory, is published in fields computer graphics to politics, mathematics to manufacturing, and much more. Evan has founded or co-founded over half a dozen companies to commercialize different technologies. Enjoy the show! We speak about: [01:15] How Evan started in the world of data [09:45] Zoom out to solve technical roadblocks [12:10] Examples of how Evan zoomed out [14:25] Why is zooming out a challenge for data scientists? [18:00] Focus on simplification [22:45] Taking opportunities that present themselves [27:00] Measures of success during a project [31:00] The process of a case study [36:05] Getting users to adopt new technologies [40:00] Innovation Tools [47:20] Evan’s proudest moment [49:40] Challenges for the future of machine learning [51:30] Get soft skills Resources: Evan’s LinkedIn: https://www.linkedin.com/in/eshellshear/ Innovation Tools: https://amzn.to/2OrrAsj Quotes: “Take a step up and over to look at the problem in a new direction.” “It is in our human nature to overcomplicate things.” “I need to help the company understand what the true problem is.” “Take a low-risk approach to solve your client’s problem.” Thank you to our sponsors: Fyrebox - Make Your Own Quiz! RMIT Online Master of Data Science Strategy and Leadership Gain the advanced strategic, leadership and data science capabilities required to influence executive leadership teams and deliver organisation-wide solutions. Visit online.rmit.edu.au for more information And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show! --- Send in a voice message: https://anchor.fm/datafuturology/message

 #71 Creating Effective Data Science Presentations with Rachel Fojtik – Director of Analytics and Performance | File Type: audio/mpeg | Duration: 3390

Rachel Fojtik is an Experienced Senior leader in Analytics, influencing change in behaviour, company culture, and improvement with analytics. Managing high performing teams that deliver across a myriad of knowledge areas. She is passionate about delivering information that sees results, using collaborative design and development. A demonstrated history of setting up teams that deliver end to end business intelligence implementations. Cross-industry experience in healthcare, telecommunications, the financial services industry, travel and tourism, and energy. Enjoy the show! We speak about: [01:15] How Rachel started in the data space [08:40] The motivation behind Rachel’s trailblazing [11:30] The metrics Rachel was helping optimize [14:10] Working with the management director vs. operational work [16:45] Data matching at Diner’s Club [22:15] Using a minimalist view [24:45] Find the best way – don’t just stick with what you know [28:45] If something is well presented, it is more likely to be trusted [35:00] What is a product manager? [42:50] An organic governance in the workplace [46:15] Rachel’s role as Director of Analytics and Performance [53:00] Building and working on a network [54:10] Do what you’re passionate about Resources: Rachel’s LinkedIn: https://www.linkedin.com/in/rachel-fojtik-78321199/ Quotes: “I created an input tool where a user could design the layout of their input form.” “I’ve always tried to go with a minimalist view.” “If your presentation is way too busy, it is difficult to take a story from that information.” “Consider where the eye goes first when creating a presentation.” Thank you to our sponsors: Fyrebox - Make Your Own Quiz! RMIT Online Master of Data Science Strategy and Leadership Gain the advanced strategic, leadership and data science capabilities required to influence executive leadership teams and deliver organisation-wide solutions. Visit online.rmit.edu.au for more information And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show! --- Send in a voice message: https://anchor.fm/datafuturology/message

 #70 Making Black Box Models Explainable with Christoph Molnar – Interpretable Machine Learning Researcher | File Type: audio/mpeg | Duration: 3312

Christoph Molnar is a data scientist and Ph.D. candidate in interpretable machine learning. He is interested in making the decisions from algorithms more understandable for humans. Christoph is passionate about using statistics and machine learning on data to make humans and machines smarter. Enjoy the show! We speak about: [02:10] How Christoph started in the data space [09:25] Understanding what a researcher needs [15:15] Skills learned from software engineers [16:00] Statistical consulting [19:50] Labeling data [23:00] Christoph is pursuing his Ph.D. [29:00] Why is interpretable machine learning needed now? [31:00] Learning interpretability [33:50] Accumulated local effects (ALE) [37:00] Example-based explanations [39:15] Deep learning [43:35] The illustrations in Interpretable Machine Learning. [49:50] How Christoph maximizes the impact of his time Resources: Christoph’s LinkedIn: https://www.linkedin.com/in/christoph-molnar-63777189/ Christoph’s Website: https://christophm.github.io Interpretable Machine Learning: https://christophm.github.io/interpretable-ml-book/ Quotes: “Always look at the process when labeling data.” “After each chapter of my book, I publish it and get feedback.” “I randomly read a lot of papers and structure the knowledge to fit them together.” “I express what I want easier with illustrations in my book.” Thank you to our sponsors: Fyrebox - Make Your Own Quiz! RMIT Online Master of Data Science Strategy and Leadership Gain the advanced strategic, leadership and data science capabilities required to influence executive leadership teams and deliver organisation-wide solutions. Visit online.rmit.edu.au for more information And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show! --- Send in a voice message: https://anchor.fm/datafuturology/message

 #69 Creating Cultural Transformations Using Data Science Leaders with Bülent Kiziltan – Head of Data Science & Analytics and Chief Data Scientist | File Type: audio/mpeg | Duration: 2899

Dr. Bülent Kiziltan is an AI executive and an accomplished scientist who uses artificial intelligence to create value in many business verticals and tackles diverse problems in disciplines ranging from the financial industry, healthcare, astrophysics, operations research, marketing, biology, engineering, hardware design, digital platforms, to art. He has worked at Harvard, NASA, and MIT in close collaboration with pioneers of their respective fields. In the past 15+ years, he has led data-driven efforts in R&D and built multifaceted strategies for the industry. He has been a data science leader at Harvard and the Head of Deep Learning at Aetna leading and mentoring more than 200 scientists. Enjoy the show! We speak about: [02:00] Bülent’s background [05:50] The transition from astrophysics to business [08:45] Data leaders need technical experience [12:45] Academics still need soft skills [19:20] What data science can offer organizations [23:50] Addressing causal inferences [25:30] Recommendations for implementing culture in the workplace [30:00] How a leader should balance priorities [36:10] Challenges Bülent currently faces in the industry [38:15] Hierarchy in the startup space [40:45] What Bülent loves about data science [42:45] Future data challenges Resources: Bülent’s Website: http://www.kiziltan.org/ Bülent’s LinkedIn: https://www.linkedin.com/in/bulentkiziltan/ Quotes: “Culturally, I was surprised by the mindset of business leaders.” “We asked individual members of the data science group to come up with their own ideas that can be implemented in the day-to-day business operations.” “A diverse team is critically important for the business.” “All companies will become AI companies in one way or another.” Thank you to our sponsors: Fyrebox - Make Your Own Quiz! RMIT Online Master of Data Science Strategy and Leadership Gain the advanced strategic, leadership and data science capabilities required to influence executive leadership teams and deliver organisation-wide solutions. Visit online.rmit.edu.au for more information And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show! --- Send in a voice message: https://anchor.fm/datafuturology/message

 #68 How To Build Award Winning Data Products with Nick Blewden – Head of Data Product Development | File Type: audio/mpeg | Duration: 3082

Nick loves playing with, analyzing and visualising data and gets a massive kick out of the change it can bring to people, businesses, and the world. At Lloyd's of London, Nick leads a team of great designers and developers helping people automate processes and get more insight from their data. He gets a buzz from saving others time and surprising them with what surprising insights lie in their data. Out of the office, Nick is keen to mentor or share his experiences and enjoys speaking at events or conferences. In this episode, Nick discusses some of his favorite projects and describes issues he has faced being part of various teams. When overcoming team obstacles, he listens to every person in the group. If you do not listen to people, then you cannot persuade someone that you are a good guy. Transparency is also essential; explain what knowledge you bring and the processes that you do. People can pick up on integrity, but they can also pick up on suspicion. Currently, Nick works at Lloyd's of London, the world's leading insurance market providing specialist insurance services to businesses in over 200 countries and territories. He has helped establish a vision and strategy for Business Intelligence within Lloyd's of London and external market published insight. He delivers automated online MI apps to a range of business functions through a roadmap of strategic change while creating a training structure to develop BI analysts across the business. Nick's data product development team has to work with other teams at Lloyd's of London to create the best products for their customers. They communicate with the innovation team to understand the research. They also work with all the different modelling teams to access their expertise and bounce ideas around with each other. Before working at Lloyd's of London, Nick was self-employed; it taught Nick a lot about customer service, collaboration, and teamwork. Later, Nick explains common mistakes in developing data products, winning global hackathons, and what excites him most about the future of data. Enjoy the show! We speak about: [01:10] How Nick started in the data space [07:00] The evolution of data warehousing [11:45] Nick’s favorite projects [14:45] Navigating team issues [15:30] Solving problems at Lloyd’s of London [20:00] Lloyd’s of London customers [25:40] Interaction with other teams [30:30] Working for yourself [35:45] Common mistakes in developing data products [38:50] Winning global hackathons [40:20] What excites Nick most about the future of data [45:45] Nick’s proudest moments Resources: Nick’s LinkedIn: https://www.linkedin.com/in/nicholasblewden Thank you to our sponsors: Fyrebox - Make Your Own Quiz! RMIT Online Master of Data Science Strategy and Leadership Gain the advanced strategic, leadership and data science capabilities required to influence executive leadership teams and deliver organisation-wide solutions. Visit online.rmit.edu.au for more information And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show! --- Send in a voice message: https://anchor.fm/datafuturology/message

 #67 12 Questions to Ask in Mentoring Sessions with Felipe Flores – Founder & Podcast Host | File Type: audio/mpeg | Duration: 1437

In this episode, Felipe talks about an aspect of leadership, the one-on-one mentorship and feedback session with people on your team. To start, ask your team member what is on their mind and in general, how have things been going? If they do not have anything pressing they want to discuss in the sessions, Felipe turns to set of twelve questions. The first question is what you are most proud of that you have done since we last caught up? These questions are designed as tools for the team member to recognize the need for self-assessment. What they say is not essential; really, the follow-up questions are more necessary to help them uncover themselves. Next, ask what the team member could have done better since the last time you talked. By allowing them to evaluate and think of improving continually, they can learn faster and become more efficient in their work. The next few questions require a broader perspective and ask about the team as a whole. It should be clear that everything is a team effort, and all member’s ideas are respected and heard by others on the team. After the team questions, head back to questions about the person and ask what they would like to work on or improve? Then, the next issue will take a lot of trust and rapport with your team member, ask what is one thing that is true that you think I do not want to hear? Question nine is how I can help you to do better? This question has taught Felipe that he is good at the big picture but needs to focus on the details and how the team might achieve the big picture. Finally, the last three questions are asking what they like best and least about the organization and if they are happy at the moment. Enjoy the show! We speak about: [01:35] Open-ended questions [02:15] What are you most proud of that you have done since we last caught up? [03:45] What could you have done better? [05:30] Questions about the team [10:00] What would you like to work on or improve? [11:40] What is one thing that is true that you think I do not want to hear? [13:10] How can I help you to do better? [15:00] What do you like the least about the team? [16:00] What do you like best about the team? [16:35] Are you happy at the moment? Resources: Saturday Night Live Quotes: “One of the best and quickest ways to learn is to evaluate your efforts continually.” “Understand the human behind the data scientist.” “You don’t need to be fixing every person’s problem, but everyone needs help every now and again.” Thank you to our sponsors: Fyrebox - Make Your Own Quiz! RMIT Online Master of Data Science Strategy and Leadership Gain the advanced strategic, leadership and data science capabilities required to influence executive leadership teams and deliver organisation-wide solutions. Visit online.rmit.edu.au for more information And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show! --- Send in a voice message: https://anchor.fm/datafuturology/message

 #66 How to Structure a Data Analytics Team with June Dershewitz – Director of Analytics | File Type: audio/mpeg | Duration: 3451

June Dershewitz has spent her career driving analytics strategies for major businesses. She's currently Director of Analytics at Twitch, the world's leading video platform and community for gamers (a subsidiary of Amazon). As an analytics practitioner, she builds and leads teams that focus on marketing analytics, product analytics, business intelligence, and data governance. In her prior life as a consultant, she was a member of the leadership team at Semphonic, a prominent analytics consultancy (now part of Ernst & Young). As a long-standing advocate of the analytics community, she was the co-founder of Web Analytics Wednesdays; she's also a Director Emeritus of the Digital Analytics Association and a current Advisory Board Member at Golden Gate University. She holds a BA in Mathematics from Reed College in Portland, Oregon. Enjoy the show! We speak about: [01:40] How June started in the data space [08:20] Solving problems in startups [09:45] Getting a holistic view in the workplace [11:20] Feeling unsure about owning a piece of work [15:30] Business intelligence skillsets for data scientists [19:35] Clear understanding of data roles in the workplace [20:55] An overview of June’s teams’ structures [27:10] Managing career transitions with the hub and spoke model [29:25] Assigning each person a technical buddy [32:10] The data quality journey [41:40] Evolution of data quality at Twitch [48:00] Becoming involved in the data science community [53:10] Other ways June stays involved in her communities [55:20] Advice for breaking into the data science field Resources: June’s LinkedIn: https://www.linkedin.com/in/jdersh Non-Invasive Data Governance: The Path of Least Resistance and Greatest Success Data Governance: How to Design, Deploy and Sustain an Effective Data Governance Program Quotes: “The thing about being a data person at that time was we just had to figure it out.” “I was the vice president of everything that needed to get done.” “At Twitch, we don’t have a clear definition of what a data engineer means.” “We chose to move to an organization model that is hub and spoke.” “Data governance can mean lots of things to lots of people.” Thank you to our sponsors: UNSW Master of Data Science Online: studyonline.unsw.edu.au Datasource Services: datasourceservices.com.au or email Will Howard on will@datasourceservices.com.au Fyrebox - Make Your Own Quiz! And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show! --- Send in a voice message: https://anchor.fm/datafuturology/message

 #65 Using the Love@Work Method to Improve Workplace Culture with Olivia Parr-Rud – Speaker, Award-Winning Author, and Data Scientist | File Type: audio/mpeg | Duration: 3397

Olivia is an internationally known thought-leader, speaker, best-selling and award-winning author, and a data scientist who focuses on the interplay between technology, corporate leadership, and personal growth and happiness.  Throughout her career, she has blended analytic tools and holistic organizational practices to deliver successful solutions for her clients. As a lifelong spiritual seeker, Olivia began to see patterns that revealed the importance of love as a driver of business success. In this episode, Olivia explains why she changed her major to statistics in grad school. Once she completed her degree, she joined a bank in San Francisco. Olivia built a model using logistic regression for the bank. It saved the company 17 million dollars a year in mail expense, making her an instant hero. Her desktop computer had a 500-megabyte hard drive when she was running SAS she couldn’t get into any other programs. Financial services had a vibrant climate for modelling because the behavioral data was so reliable. Behavioral data is so powerful because if a person has done something before, they are more likely to do it again. Enjoy the show! We speak about: [01:40] How Olivia started in the data space   [07:50] Data in the financial services industry  [08:50] Oliva’s career history   [13:35] Starting a consulting business  [17:10] Tips for explaining data science to non-technical people [18:30] Becoming a published author  [24:45] Learning about Holacracy   [29:00] Balancing Holacracy and teamwork  [31:40] Combing data and human skills  [40:20] The Love@Work Method [47:15] One of Oliva’s professional fails    [51:10] Using LEAP (love, energy, audacity, and proof) [54:30] Following our intuitions  Resources: Oliva’s Website: www.lovemakeityourbusiness.com Data Science Consulting: www.oliviagroup.com  My Big ‘Why’ - https://tinyurl.com/LOVENEWCOMPETITIVEEDGE LOVE@WORK now available at https://tinyurl.com/OLIVIAPRLOVEATWORK - A Silver Nautilus Book Award-Winner  The LOVE@WORK MethodTM now available at https://tinyurl.com/TheLOVE-WORKMethod What is your Corporate Love Quotient? Find out here www.corporatelovequotient.com  Oliva’s Social Media: Facebook: https://www.facebook.com/LoveMakeItYourBusiness/ LinkedIn: https://www.linkedin.com/in/oliviagroup/ Twitter handle: #OliviaParrRud   YouTube: www.OliviaOnYouTube.com Instagram: Love.MakeItYourBusiness Now you can support Data Futurology on Patreon!   https://www.patreon.com/datafuturology  --- Send in a voice message: https://anchor.fm/datafuturology/message

 #64 Intersections of Analytics, AI, Linguistics and Culture with Prashant Natarajan – Principal, AI & Analytics | File Type: audio/mpeg | Duration: 3392

Prashant Natarajan has 18+ years’ experience in building EMRs, ERP, big data platforms, actionable analytics, and machine/deep learning applications. Before joining Deloitte, he served in hands-on global consulting and product leadership roles at H2O.ai, Oracle, McKesson Payer Solutions, Healthways, and Siemens. Prashant is Co-Faculty Instructor of Data Science and AI at Stanford University School of Medicine, Palo Alto, CA, USA. He volunteers as an industry expert and guest lecturer at leading Australian universities. Prashant serves as an industry advisor at the CIAPM computer vision project in University of California San Francisco, Council for Affordable Health Coverage, and Pistoia Alliance Center for Excellence in Artificial Intelligence.   In this episode, Prashant describes how essential human interaction is for success. In a technology-heavy space, human interaction and linguistics were not very common. Instead of complaining about it, Prashant went and got his masters to focus on English in the technology space. To have success, we need a clear understanding of culture. Culture is language, and language at its core is mathematics. How do we interact with people to figure out what their strengths are? Prashant considers himself the luckiest person on earth to have the experiences he has had in his career.  Enjoy the show! We speak about: [01:25] How Prashant started in the data space  [03:45] Studying communications and linguistics   [08:45] Mentoring young professionals   [11:45] Work with people who are smarter than you   [15:00] Merging business problems with data science   [19:45] The value business leaders see in data   [25:00] Advice for companies who are moving into data-driven products [29:45] What excites Prashant about the future of data  [34:05] Horizontal capabilities  [37:20] The use of machine learning in healthcare   [44:20] Improving product development  [48:40] Prashant’s proudest moment [50:15] The manufacturing industry  [52:20] We learn more from our failures than our successes  Resources: Prashant’s LinkedIn: https://www.linkedin.com/in/natarpr/ Demystifying Big Data and Machine Learning for Healthcare (Himss Book) Quotes: “Human interaction is the most key determiner of success or not.” “Today, we have the technology that has caught up with the human need.” “Data science is increasingly a horizontal capability that will impact all of us.” “I celebrate relationships because they allow me to learn.” Now you can support Data Futurology on Patreon!   https://www.patreon.com/datafuturology  Thank you to our sponsors:  UNSW Master of Data Science Online: studyonline.unsw.edu.au  Datasource Services: datasourceservices.com.au or email Will Howard on will@datasourceservices.com.au  Fyrebox - Make Your Own Quiz! And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show! --- Send in a voice message: https://anchor.fm/datafuturology/message

 #63 Set Yourself Multi-Year Professional Challenges with Felipe Flores – Founder & Podcast Host | File Type: audio/mpeg | Duration: 3855

In this episode, Anthony Ugoni, one of Australia’s more prominent leaders in analytics interviews Felipe. Felipe came to Australia as a backpacker and ended up falling in love with the place. With Spanish as his first language, the only English he could say was the jacket is black. Then, Felipe explains some of his odd jobs and working freelance IT. At university, Felipe wanted to specialize in data, but all of his friends told him it was dead. So, he ended up specializing in hardware, even though all of his work was in data. When Felipe went to do his thesis, he happened to stumble into a project involving brain wave activity. The electrical engineer did all the research and design, the signals would be passed to Felipe’s computer, where he made his first application of machine learning.  Then, Felipe explains how he and a colleague of his made the decision to quit their jobs at a small consulting firm. They decided to start their own firm, despite knowing very little about business. The first year they almost went bankrupt about four times and made lots of mistakes. They wanted to be in analytics but were unsure how to sell their services. The two spent six months creating a piece of software. When they went to show prospects they found out people did not like the entire product. So they decided to focus on their consulting business.  Enjoy the show! We speak about: [02:40] Felipe’s background  [06:10] Education and specializations [14:30] Quick delivery of value   [17:20] A series of odd jobs and IT freelancing   [24:20] Setting up his own consulting company   [33:15] Highs and lows of Clear Blue Water [37:30] Executive Director & Head of Data Science at ANZ [47:55] Supportive and open culture at work  [52:40] Understanding the business at a new job [54:45] Inspiration behind Data Futurology [62:00] Explainable AI  Resources: Felipe’s LinkedIn: https://www.linkedin.com/in/felipefloresanalytics/?originalSubdomain=au Episode #21 Antony Ugoni: https://www.datafuturology.com/podcast/21 Quotes: “If I’m an engineer, people will think I’m smart.” “A colleague of mine and I decided to set up our own consulting company. Professionally, it was the best and worst thing I’ve ever done.” “Sales is built on trust and a human connection.” “I had not done a good job of being a leader and creating a culture.” “How can we make data scientists today, the CEOs of tomorrow?”  Now you can support Data Futurology on Patreon!   https://www.patreon.com/datafuturology  Thank you to our sponsors:  UNSW Master of Data Science Online: studyonline.unsw.edu.au  Datasource Services: datasourceservices.com.au or email Will Howard on will@datasourceservices.com.au  Fyrebox - Make Your Own Quiz! And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show! --- Send in a voice message: https://anchor.fm/datafuturology/message

 #62 Full Stack Data Science with Gregory Hill – Global Head of Analytics | File Type: audio/mpeg | Duration: 2983

Dr. Gregory Hill leads the Analytics function at Brightstar's Global Services division, developing and delivering their data & analytics strategy, innovation programs, and product development initiatives. He works across their lines of business, including supply chain optimization, product portfolio management, financial services, buy-back and trade-in, leasing, and omnichannel solutions. He also manages Brightstar's analytics team in support of their key global accounts with pre-sales, solution design, and service delivery. His expertise is in the application of advanced analytics techniques (including machine learning, predictive modelling, mathematical optimization, econometrics, and operations research) to commercial problems. These applications span forecasting, pricing, fraud, market segmentation, customer satisfaction, and propensity modelling. In this episode, Gregory explains how he started in the data space. He was aware of all the theoretical work being done around data but did not know how it worked in an industry aspect. The real challenge of putting mathematical models to practice lies in the organizational and people elements of it. Computer science and electrical engineering do not teach you how to overcome organizational challenges and individual motivations and incentives. Going back to get his Ph.D., Greg wanted to do something requiring qualitative research. So he targeted informational systems and economics. His fieldwork leads him to interview executives of larger banks, publicly listed companies, and government agencies. He came up with an economic framework that improved customer data quality.  Enjoy the show! We speak about: [02:00] How Greg started in the data space [11:10] Leaving academics and getting involved in the industry   [13:20] Greg’s work background [18:25] The four P’s of marketing [20:40] Transiting from gut instinct to a data-driven approach [27:55] Thinking through cause and effect  [30:45] What Greg’s team looks like [39:00] Lessons learned from managing data scientists   [42:25] Active in local data science meetups + guest speaking   [44:25] Working globally + peeling back opportunities to use data science techniques Resources: Greg’s LinkedIn: https://www.linkedin.com/in/gregoryhill/?originalSubdomain=au Brightstar: https://www.brightstar.com Quotes: “My thesis was not a project; it was a lifestyle.” “I didn’t want to be an academic, I wanted to get back into the industry.” “It was a combination of arrogance and laziness.” “At the end of the day, it boils down to if I change X, will Y change?” Now you can support Data Futurology on Patreon!   https://www.patreon.com/datafuturology  Thank you to our sponsors:  UNSW Master of Data Science Online: studyonline.unsw.edu.au  Datasource Services: datasourceservices.com.au or email Will Howard on will@datasourceservices.com.au  Fyrebox - Make Your Own Quiz! And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show! --- Send in a voice message: https://anchor.fm/datafuturology/message

 #61 Data Science Strategy in the Military, Startups and Tech/Digital Leaders with Sveta Freidman - Data Analytics & Science Director/Mentor | File Type: audio/mpeg | Duration: 3047

Sveta Freidman is a data scientist and business intelligence leader with extensive experience in consulting and client-based environments. She has a vast experience working in different industries, including gambling, retail, health, and online businesses (startups). Sveta is a data strategist with a passion for connecting people to the data they need to make decisions, build better products, and execute marketing strategies.  In this episode, Sveta explains why she decided to study statistics, she had a passion for mathematics. During her time in Israel’s military, she collected data from different places and made sense from it. Her commercial experience comes from various startups she joined. When joining a startup, you have to wear many hats. Sometimes you have to be a data engineer, data scientist, or a data analyst. Then, Sveta moved to Australia and found a startup, Envato, where she built all the data from scratch. Enjoy the show! We speak about: [01:40] How Sveta started in the data space  [07:15] Sveta’s professional background   [17:10] Investing in local talent    [20:45] How to hire for a startup [24:30] Questions for hiring interviews  [29:25] Working for Carsales  [31:55] People not trusting the data   [35:20] Solving the issue of trust  [40:30] Finding bias in the data  [44:50] Make sure you look at the data every day  Resources: Sveta’s LinkedIn: https://www.linkedin.com/in/sveta-freidman-5981593 Carsales: https://www.carsales.com.au Quotes: “Statistics is everywhere.” “You can be a great data scientist, but you need to understand the culture.” “I give the candidate a business problem to see how they will react to it.” “Your algorithms are good as long as your data is good.” Now you can support Data Futurology on Patreon!   https://www.patreon.com/datafuturology  Thank you to our sponsors:  UNSW Master of Data Science Online: studyonline.unsw.edu.au  Datasource Services: datasourceservices.com.au or email Will Howard on will@datasourceservices.com.au  Fyrebox - Make Your Own Quiz! And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show! --- Send in a voice message: https://anchor.fm/datafuturology/message

 #60 Building Self-Driving Cars in Silicon Valley with Vladimir Iglovikov, Ph.D. – Senior Computer Vision Engineer and Kaggle Grandmaster | File Type: audio/mpeg | Duration: 3780

Vladimir Iglovikov graduated from university with a degree in theoretical physics, he moved to Silicon Valley in search of a data science role in the industry. This led him to his current position in Lyft’s autonomous vehicle division where he works on computer vision related applications. In the past few years, he has invested a lot of time in Machine Learning competitions leading to his title of Kaggle Grandmaster. In this episode, Vladimir explains how difficult it was to find work in Silicon Valley. He had harsh requirements for a salary, no one looked at his resume. Companies in Silicon Valley are willing to pay big bucks, but at the same time, they require the person to be skilled in software engineering, machine learning, and statistics. His biggest issue when applying for jobs was assuming that all people are similar to the people in academics. At his interviews, he felt no connection with the interviewers. After sending his resume to over 200 different companies, someone finally bit just before his visa expired. Vladimir worked at Bidgely for 8 months then moved to TrueAccord and eventually got his job at Lyft.  Enjoy the show! We speak about: [02:00] How Vladimir started in the data space [12:30] Transferring from academia to industry  [21:40] Benefits of having soft skills    [25:45] How Vladimir manages stress  [31:30] Kaggle is like lifting weights  [35:30] The hiring process for data scientists   [40:45] Excitement for machine learning  [46:00] Autonomous driving    [47:55] Pursuing a startup [51:40] Aiming to maximize mistakes in a day  [61:00] Social life comes first  Resources: Vladimir’s LinkedIn: https://www.linkedin.com/in/iglovikov/ Kaggle: https://www.kaggle.com/iglovikov Now you can support Data Futurology on Patreon!   https://www.patreon.com/datafuturology  Thank you to our sponsors:  UNSW Master of Data Science Online: studyonline.unsw.edu.au  Datasource Services: datasourceservices.com.au or email Will Howard on will@datasourceservices.com.au  Fyrebox - Make Your Own Quiz! And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show! --- Send in a voice message: https://anchor.fm/datafuturology/message

 #59 Creating the Link Between Business and Data with Tony Gruebner - GM Analytics, Insights and Modelling | File Type: audio/mpeg | Duration: 3223

Tony Gruebner is the GM Analytics of Insights and Modelling and the Exec Sponsor of Personalisation at Sportsbet. He established a department of 40+ skilled analysts and data scientists tasked with creating innovative data products focused at improving the experience for their customers and supporting the business by providing relevant and timely information and insights that steer decision making across all levels of the business. He has served on the Executive Leadership Team from 2016. In this episode, Tony explains how he started in data and what led him to get his job at Sportsbet. Tony got a call from a recruiter asking if he wanted to do work with analytics, in a company that does sports and is heavily digital. All of those factors checked the box for Tony, and he took the entry-level analyst role. Over time, the need for analytics has grown, so he has been able to develop some analytics teams.  Enjoy the show! We speak about: [01:20] How Tony got started in data [08:20] Tony’s skills come from the commercial side [11:10] Linking data science and the business [14:30] Communicating how data science works [17:00] Steps to getting others to understand data science [20:40] Getting the best talent for your team [24:00] Structuring teams and the department [28:10] Transiting from analytical roles to commercial roles  [35:30] Working on global expansion [38:10] Solving with artificial intelligence [42:30] Passionate about using numbers to reach an outcome [44:00] Modelling failures with Sportsbet  [47:50] Imposter syndrome in data science   [50:05] Data science is rapidly changing and exciting Resources: Tony’s LinkedIn: https://www.linkedin.com/in/gruebz/ Sportsbet: https://www.sportsbet.com.au Tony’s Twitter: https://twitter.com/gruebz?lang=en Quotes: “There is no one path that always works.” “There are literally thousands of things data scientists couldn’t potentially tackle in any business.” “If you’re not making mistakes, then you aren’t pushing the envelope hard enough.” “Not having imposter syndrome is a sign of lack of knowledge.” Now you can support Data Futurology on Patreon!   https://www.patreon.com/datafuturology  Thank you to our sponsors:  UNSW Master of Data Science Online: studyonline.unsw.edu.au  Datasource Services: datasourceservices.com.au or email Will Howard on will@datasourceservices.com.au  Fyrebox - Make Your Own Quiz! And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show! --- Send in a voice message: https://anchor.fm/datafuturology/message

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