Welcome to the official podcast for Data Science Salon!
The Data Science Salon series is a unique vertical focused conference which brings together specialists face-to-face to educate each other, illuminate best practices, and innovate new solutions in a casual atmosphere.
In our podcast, our Senior Content Advisor Q McCallum holds deep, engaging interviews with guests ranging from DSS speakers, authors of data science books, and professionals whose work interacts with the AI world. All of them bring their unique perspective on trends and business use cases in the world of data science, machine learning, and AI.
NEW – Episode Thirty
Episode Twenty Nine
If you’ve been in the data game long enough, you’ve probably seen this before: a stakeholder or product owner approaches you with a project that’s 95% done, and they’d like you to … “sprinkle some AI on it.” They’ve heard that this “AI” thing can be useful so they want some of it in their latest effort.
Data scientist-turned-product person Noelle Saldana has experienced the “sprinkle some AI on it” request more times than she’d care to remember. Our Senior Content Advisor Q McCallum met up with Noelle to explore this phenomenon. How does this happen? (Hint: “corporate FOMO.”) What should you do when stakeholders insist on implementing AI that isn’t actually going to help? What about when your data scientist peers seem like they’re doing this for the sake of “résumé-driven development?”
Ultimately, the pair work through the bigger issue: how do you make peace with companies throwing money at AI like this? And how can these companies use this approach to their advantage?
As a bonus, Noelle shares how she made the move from a data scientist role into product management. If this path sounds interesting to you, take a listen.
Episode Twenty Eight
Sometimes the most valuable data IN your company … is the data LEAVING your company. That’s Solomon Kahn’s view on data products, as well as the premise behind his latest venture: Delivery Layer. For this episode, our Senior Content Advisor Q McCallum reached out to Solomon to check in on the new startup, and to tap his expertise in the world of data products.
Solomon’s been at this a while. He’s run high-revenue data products in some notable places, including Nielsen. Over the years he’s learned a lot and we’re excited for him to share some of that hard-earned knowledge here on the show.
In this extended conversation, the two explore: the reasons why building a data product is different (and, in many ways, more difficult) than building traditional software products; how the people involved can impact the outcome; why a good sense of risk management can make all the difference; and what purple cars have to do with all of this. (No, seriously. Purple cars.)
Along the way, the pair talk about the early days of the data field, and how much it has changed.
Episode Twenty Seven
Our show host and Senior Content Advisor, Q McCallum, has been thinking a lot about what he calls “moving beyond the point estimate” in ML modeling. That usually starts with seeing the world in terms of statistical distributions, and running simulations to get a more robust picture of a model’s results.When he had questions, he reached out to his old friend James “JD” Long for answers. James is a self-described “agricultural economist, quant, stochastic modeler, and cocktail party host” who does a lot of work in R, Python, and AWS. Through his work in the reinsurance field he has developed deep knowledge of simulations and probabilistic thinking, as well as an ability to explain these topics in plain language.
Episode Twenty Six
The roles of economists in data science, with Dr. Amar Natt
We’ve all heard the term “economist,” sure. But exactly what does and economist do? And as economics is a very data-driven field, where does their work intersect with data science, machine learning, and AI?
To answer that question, Senior Content Advisor Q McCallum spoke with Amar Natt, PhD. She’s an economist at Econ One Research, and her work focuses on advanced analytics and predictive modeling. Does that sound like ML to you? Well, Amar explains that it’s similar in some ways, different in others. From there, she tells us about techniques economists can learn from data scientists, and what data scientists can pick up from econ. (Hint: “causal inference.” You heard it here first.)
Episode Twenty Five
ML at The Home Depot with Pat Woowong: The Falloff Model and Lead Scoring
When people think about The Home Depot, they probably think more about lumber and tile than they do ML models. Sure, there is plenty of lumber. But machine learning also plays a key role in the business, in places that customers can see as well as the behind-the-scenes operations.
Senior Content Advisor Q McCallum met up with Pat Woowong, Director of Data Science at The Home Depot, to explore how the company mixes their very rich dataset with domain knowledge to employ machine learning deep inside the business. To frame this, he walked me through the Falloff model and Lead scoring, two projects that his team deployed to address the unique challenges of a company that handles both retail and services.
During our conversation, we discussed: understanding where models fit into the bigger business picture; using expert domain knowledge to drive feature selection and feature engineering; the value of process; and, to top it off, what it’s like to work at The Home Depot.
Episode Twenty Four
Coffee Chat: Inspiring ML Use Cases in Retail Delivering Measurable Impact
This episode is a coffee chat recording from DSS Virtual in May 2022. Charles Irizarry (Phygital) and Ankita Mangal (P&G) share in war stories of ML use cases they use in retail and eCommerce scenarios, brokering data, and protecting the important principles of data ethics and privacy. Ankita shares the digital transformation journey that P&G undertook, her growth together with P&G, and some of the incredible technologies P&G has developed to better serve their customers world wide.
Episode Twenty Three
Data Science and Data Engineering in the Federal Space with Dr. Pragyansmita Nayak
A lot of data scientists work in the private sector: finance, adtech, retail, and all that. Today’s guest offers her perspective on what it means to do data work in the federal space.
In this conversation, our Senior Content Advisor Q McCallum spoke with Dr. Pragyansmita Nayak, Chief Data Scientist at Hitachi Vantara Federal. They explored how different federal agencies use data and how they share datasets with each other. They also talked about how to measure operational efficiency, when you can’t rely on metrics like “profit.” And, the big question: should we release t-shirts that read “just give me my AI solution!” ?
The book Q mentioned is Army of None, by Paul Scharre.
EPISODE TWENTY TWO
SOFTWARE DEVELOPMENT SKILLS IN ML/AI
Data scientists write a lot of code, sure, but few of them come from a formal software dev background. That can lead them to struggle with slow, buggy code that ultimately holds back the company’s ML efforts. Want to write cleaner, more performant code? Looking for ways to make those model deployments more reproducible? Listen to Murium and Q explore topics such as writing tests, using Docker to isolate dependencies, and learning best practices from your software developer teammates.
Episode Twenty One
COFFEE CHAT: MODEL INTERPRETABILITY AND HOW TO CREATE TRUST IN AI PRODUCTS
This episode is a recording of the panel conversation at the virtual Data Science Salon in April 2022, which focused on AI & machine learning applications in the enterprise.
Charles Irizarry (CEO & Co-Founder at Strata.ai) had the chance to talk to Amarita Natt (Managing Director, Data Science at Econ One Research), Preethi Raghavan (VP, Data Science Practice Lead at Fidelity Investments) and Serg Masís (Climate and Agronomic Data Scientist at Syngenta) about the important topic of model interpretability and how to create trust in AI products.
Coffee Chat: DSS Hybrid Miami 2022
This episode is a recording of the coffee chat at the hybrid Data Science Salon Miami, which focused on AI & machine learning applications in the enterprise.
Charles Irizarry, CEO & Co-Founder at Strata.ai had the chance to talk to Nirmal Budhathoki, Senior Data Scientist at VMware Carbon Black and Moody Hadi, Group Manager – New Product Development & Financial Engineering at S&P Global. Tune in to hear about ML techniques they are using in their current roles, tools to put ML into production, model explainability, and future trends.
Communal Computing and AI with Chris Butler – Pt. 2
Coffee Chat: DSS Virtual 2021/12: Applying AI & Machine Learning to Finance & Technology
This episode is a recording from our recent Data Science Salon event, which focused on applying AI and ML to finance and technology. Our Senior Content Advisor Q McCallum sat down with data scientists Linda Liu (Hyrecar) and Giacomo Vianello (Cape Analytics) to talk about their work. We explored the techniques and tools for the various data projects they’re running, some of the challenges of working with geospatial data, and how they approach R&D efforts in the company. (The hint for that last one: balance, discipline, and structure rule the day. Very practical.)
AI, Product, and Uncertainty with Chris Butler – Pt. 1
Coffee Chat: DSSe Virtual 2021
Analytics vs. Data Science vs. ML Research: Economist Sonali Syngal Shares Her View
Charting a Course: from Physics PhD to Professional Data Scientist with Dr Resham Sarkar
Data Monetization Strategies with Micheline Casey
Software Testing, Performance Tuning, and Code Handoff for Data Scientists
Coffee Chat at DSSVirtual for Healthcare, Finance & Technology
We recorded this episode at our February 2021 Data Science Salon Virtual on Healthcare, Finance & Technology. Formulated.by’s Senior Content Advisor, Q McCallum, sat down with Ayda Farhadi, Senior Data Scientist at UPS, and Vasileios Stathias, Lead Data Scientist at Sylvester Comprehensive Cancer Center to discuss applying AI to healthcare.
Trading, Risk, and Reinsurance with Otakar Hubschmann
Our Senior Content Advisor Q McCallum sat down with Otakar Hubschmann, Head of Applied Data at TransRe, to talk about ML/AI in the world of reinsurance. They take a deep dive into the insurance industry and the role reinsurance plays there, with a side-trip to show how this differs from the quantitative finance you see in hedge funds. Along the way, Otakar offers his favorite tips for hiring data scientists. (Whether you’re applying for a job, or hiring for one, take note.)
Virtual Coffee Chat: Live from DSS Virtual
We recorded this episode at our December 2020 Data Science Salon Virtual on Finance & Technology. Formulated.by’s Senior Content Advisor, Q McCallum, sat down with some new friends to discuss trends and challenges in the world of AI:
Thulasi Nambiar – Senior Manager, Marketing Data Science at Prosper, Jeff Sharpe – Senior Manager / Tech Lead at CapitalOne, Sonali Syngal – Applied Scientist and Project Lead AI Garage at Mastercard
Virtual Coffee Chat: Live from DSS Virtual
We recorded this episode at our November 2020 Virtual Data Science Salon on Retail & Ecommerce. Formulated.by’s Content Advisor, Roger Magoulas, sat down with some of the event’s speakers to talk about data science trends and challenges in retail & ecommerce.
Phillip Rossi, Head of Data Science at Shopify, Laya Shamgah, Data Scientist at Lowe’s Company, Jeffrey Yau, Head of Data Science at Walmart Labs, Samantha Cvetkovski, Data Science Manager at Mindbody
Automated Content Moderation and the Intersection of AI and Law
Today’s podcast is about the intersection of AI and the law. Formulatedby’s Senior Content Advisor, Q McCallum, spoke with Shane Glynn, an attorney who has deep knowledge of the tech and AI worlds. He’s worked for a couple of law firms that you may have heard of, and for a tech company that you have most certainly heard of.
Shane gave us an attorney’s view on AI practices, explored the ways in which an attorney can help with an AI effort, and explained the how, when, and why AI teams should involve their legal counsel. (Hint: early. Very early.) Shane also talked about the legal and technical aspects of AI-driven, automated content moderation.
At the end of the episode, Shane mentions some blog posts that Q wrote on AI lessons learned from the world of algorithmic trading. That series starts here.
Virtual Coffee Chat: Live from DSS Virtual
We recorded this episode at our September 2020 Data Science Salon virtual event on Media, Advertising, & Entertainment. Formulatedby’s Senior Content Advisor, Q McCallum, sat down with some new friends to discuss trends and challenges in the world of AI:
Anne Bauer – Director of Data Science at The New York Times, Yves Bergquist – Director of the AI & Neuroscience in Media Project, at USC, Kim Martin – Engineering Leader of Data Science and Engineering at Netflix, Dominick Rocco – Data Scientist at phData
Mission and Purpose in Data Science: Lessons from the Military and Intelligence
How can mission and purpose drive a data professional? And what happens when we can no longer trust the data that’s presented to us?
Richard Dunks served as a member of the US Army and the intelligence community (IC), where he honed skills that he now uses in his civilian pursuits as a data scientist, trainer, and educator. He recently caught up with Q McCallum (Senior Content Advisor at Formulatedby, the company behind Data Science Salon) to talk about what his time in the IC taught him about data analysis, having a sense of mission, and what it means to lose trust in data.
Marcello La Rocca on Algorithms and Data Structures
The term “algorithms” has several meanings, from machine learning models to tools of Wall St traders. Then there’s the classic computer science definition: a set of instructions for solving problems. Think “simulated annealing,” “evolutionary computing,” or “LRU cache.” These are the sort of algorithms we’ll explore today.
Jean-Georges Perrin on Spark and Data Quality
Our guest for this episode is Jean-Georges Perrin, the author of Spark in Action, 2nd edition. We talk about his career path (he’s been doing “big data” since before the term existed), what inspired him to write Spark in Action, and where Spark fits in your company’s data efforts. He also shares his thoughts on data quality.
Applications of Data Science in Media & Entertainment
The Media and Entertainment industry has undeniably been heavily disrupted by changes in technology. Listen as Ayan Battacharya, Advanced Analytics Specialist Leader at Deloitte Consulting and Harini Krishnan, Data Scientist at Capsule8, share observations they’ve garnered from their own experience on the state of data science in Media & Entertainment, live from DSS NYC 2019.
Prolific vs. private data in media advertising @ DSS NYC
In June 2019, over 200 data scientists gathered at Viacom HQ in New York to hear key industry players’ takes on what makes an effective data-driven strategy. Q McCallum, Senior Content Adviser at Formulated.by, took a deeper dive into the major topics of concern for data science when he spoke with DSS NYC speakers Lauren Lombardo, Senior Data Scientist at Nielsen and Sergey Fogelson, Vice President of Data Science and Modeling at Viacom. Listen as they speak about current practices and debate the ways in which the growth of AI will impact advertising.