Data Science Salon Podcast

Formulated.by’s Senior Content Advisor, Q McCallum, caught up with Linda Liu (Hyrecar) and Giacomo Vianello (Cape Analytics).  Our guests explored the techniques and tools for the various data projects they are running, some of the challenges of working with geospatial data, and how their companies approach data-related research efforts.

This discussion also explores the context around which we collect data, polysocial reality, design individualism, and contextual integrity.  (Yes, we covered a lot of ground in just 45 minutes.)

Because of our tight schedule, Chris and Q had to stop before they could get to their second topic.  That’s why Chris will be back in the next episode to talk about communal computing and what that means for AI.            

bio: Chris Butler is a product manager, writer, and speaker with over 20 years of product management leadership at Microsoft, Waze, KAYAK, and Facebook Reality Labs. He facilitates critical decision making for teams that build new and innovative products and created techniques like Empathy Mapping for the Machine and Confusion Mapping to create cross-team alignment while building AI products. 

He is now Assistant Vice President, Head of Product Operations, at Cognizant where he PM’s the PM experience.Learn more about Chris and his work through his Linkedin, Twitter, by reading some of his articles on Medium or watching some of his past talks on YouTube.

• Article: “Our Favorite Questions”
• Chris’s Product Mindset talk from Cognizant Softvision Product Day
• Chris’s Prototyping for AI/ML at the AI for PMs Summit
• At the end, we mentioned Chris’s articles on communal computing.  You can read these to get a head start on our next episode:
• Communal Computing intro
• Communal Computing’s Many Problems
• A Way Forward with Communal Computing
• AIxDesign Communal Computing workshop with animistic design mapping

Formulated.by’s Senior Content Advisor, Q McCallum, caught up with Vidhi Chugh (Walmart), Piyanka Jain (Aryng), and Tempest van Schaik (Microsoft).  Our guests explored the impact of the Covid-19 pandemic on hiring and retention, then shifted to a discussion on finding and serving as a mentor.

Formulatedby's Senior Content Advisor, Q McCallum, met up with Sonali Syngal to explore these questions.  Sonali is currently a data scientist at MasterCard and is about to join the team at Expedia.  She came to data science from the rather uncommon entry point of economics. In this episode we see that her career path has given her key insights on how to join this field and what are the differences between the various roles therein.(Some listeners may recognize Sonali's voice from a previous episode: she spoke at Data Science Salon in December 2020, where she also joined us for our Coffee Chat.  You can check out that episode to learn more about Sonali's take on ML/AI in the world of fintech.)

• Sonali mentioned Christopher Olah's blog, which lives at: https://colah.github.io  (Christopher was working at Google when Sonali found his blog, though he has since moved to OpenAI.)
• Sonali and Q are both fans of the 3Blue1Brown YouTube channel.  It offers clear, relatable explanations of linear algebra and neural networks: https://youtu.be/aircAruvnKk
• This is DeepLizard’s deep learning playlist that Sonali mentioned: https://youtube.com/playlist?list=PLZbbT5o_s2xq7LwI2y8_QtvuXZedL6tQU

What was it like to move from a physics lab into the data scientist's chair?  How did she find that first job? And what elements of her PhD experience have proven especially valuable in her machine learning work?  Join us in this conversation to find out.

• Dr Sarkar works on machine learning at Slice . (She's hiring!)
• You can also find her on LinkedIn.

Micheline has more than twenty years' experience at the intersection of data and money, and has been a Chief Data Officer (CDO) with 3 different organizations, leading and scaling data strategy, infrastructure, and platforms. She also led data commercialization efforts at Ford.  Her career includes early data brokers, automotive and logistics companies, financial services and insurance, health care, and energy.  Oh, and then there was that stint as the CDO of the Federal Reserve.  She's a real powerhouse in the data field and we're very happy that she was able to join us.

• You can find Micheline on LinkedIn and Twitter
• The paper Q mentioned early in this episode is Business Models for the Data Economy (McCallum, Gleason)
• The book Micheline mentioned near the end is Doug Laney's Infonomics: How to Monetize, Manage, and Measure Information as an Asset for Competitive Advantage

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:

• Michael Abrash’s Zen of Code Optimization
• Brendan Gregg’s Flame Graphs
• Matt's blog and videos
• Related to the discussion on performance enhancements in Quake, Matt has a video on “how Wolfenstein worked”
• Compiler Explorer

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.) Near the end of the episode, Otakar and Q mention some of their favorite books on risk, machine learning, and taking a quantitative approach to businesses.  Here's the list they promised:

• Against the Gods: The Remarkable Story of Risk (Bernstein)
• Acts of God and Man: Ruminations on Risk and Insurance (Powers)
• When Genius Failed (Lowenstein)
• The Smartest Guys in the Room (McLean)
• Moneyball (Lewis)
• The Big Short (Lewis)
• Models Behaving Badly (Derman)
• The Gray Rhino (Wucker)
• Bad Blood: Secrets and Lies in a Silicon Valley Startup (Carreyrou)
• The Quants (Scott Patterson)
• A Man for All Markets (Edward Thorp)
• Fortune's Formula (Poundstone)
• The Man Who Solved the Markets (Zuckerman)
• Artificial Intelligence (Norvig)
• Deep Learning (Goodfellow)
• Python Machine Learning (Raschka)
• Python for Data Analysis (McKinney)
• An Introduction to Statistical Learning: with Applications in R (James, Wittin, Hastie, Tibshirani)
• The Visual Display of Quantitative Information (Tufte)

Also, to learn more about the insurance industry, please check out David Wright's Not Unreasonable podcast.  Otakar's interview on that show is the episode from November 02, 2020.

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

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.

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.

Q McCallum (Senior Content Advisor at Formulatedby, the company behind Data Science Salon) met up with Marcello La Rocca, someone who compiled his extensive knowledge of algorithms into a rather hefty book on the topic.  In this episode, the author of Algorithms and Data Structures in Action talks about the study of algorithms, why they're useful, and how he's applied them to oversized datasets.  Marcello closes out with his thoughts on (sometimes, not) using algorithms in tech job interviews.