Data Science Salon Podcast

Data-Driven Excellence: AI and Analytics in Action with Matthew Denesuk & Jaime Russ Sep 10, 2024

In this DSS Podcast we chat with Matthew Denesuk, SVP of Data Analytics & AI at Royal Caribbean Group. Matthew shares his insights on leveraging a Center of Excellence model to drive data-driven strategies across the organization. Tune in to discover how this approach can transform enterprise business processes using AI, analytics, and data science! We’re also excited to welcome Jaime Russ, former Principal Data Scientist at Ryder System. Jaime brings a fresh perspective on data science, focusing on integrating advanced analytics and machine learning models into traditionally held concepts. Tune in as she explores the application of machine learning in corporate finance and its fascinating parallels to fleet management.

AI in Action: From Machine Learning Interpretability to Cybersecurity with Serg Masís and Nirmal Budhathoki Aug 06, 2024

In this DSS Podcast, Anna Anisin welcomes Serg Masís, Climate and Agronomic Data Scientist at Syngenta. Serg, an expert in machine learning interpretability and responsible AI, shares his diverse background and journey into data science. He discusses the challenges of building fair and reliable ML models, emphasizing the importance of interpretability and trust in AI. Serg also talks into his latest book, "Interpretable Machine Learning with Python," and provides valuable insights for data scientists striving to create more transparent and effective AI solutions. In another compelling episode, Anna sits down with Nirmal Budhathoki, Senior Data Scientist at Microsoft. Nirmal, who has extensive experience at VMware Carbon Black and Wells Fargo, focuses on the intersection of AI and cybersecurity. He shares his journey into security data science, discussing the unique challenges and critical importance of applying AI to enhance cybersecurity measures. Nirmal highlights the pressing need for AI in this field, practical use cases, and the complexities involved in integrating AI with security practices, offering a valuable perspective for professionals navigating this dynamic landscape.

AI at the Crossroads: Bias, Diversity, and Scalability with Boshika Tara and Dr. June Andrews Jul 23, 2024

In this week's DSSPodcast, Anna had a conversation with Boshika Tara, Technical Machine Learning Product Manager at H&M Group. Boshika brings over 7 years of experience in technical product development, engineering, and building large-scale ML systems in NLP and Computer Vision. In this episode, she dives into the critical issue of bias in AI, discussing various types of biases in machine learning, how to detect them, and the importance of creating more equitable teams with diverse representation to mitigate these biases. Additionally, Anna had the pleasure of hosting Dr. June Andrews, the Founder of Lat Long Labs. Dr. Andrews shares her incredible journey from leading the Style Discovery team at Stitch Fix to her role as a Tech Lead at LinkedIn. She discusses the complexities of scaling and transforming AI projects, particularly in predicting consumer preferences and enhancing product discovery.

Elevating Computer Vision and Female Voices with Alex Levinson and Sheila Beladinejad Jul 09, 2024

In this episode of the DSS Podcast, Anna Anisin introduces two powerhouse guests in the realms of AI and robotics. First, Anna welcomes Alex, Principal Algorithms/AI Engineer at Elbit Systems of America, based in Miami. Alex shares her journey into the field of AI, particularly computer vision, and discusses common use cases, pitfalls, and success stories in sourcing and improving data for computer vision models. She also offers valuable recommendations for data scientists starting out in the field and highlights an exciting trend in AI that she's currently following. Next, Anna introduces Sheila Beladinejad, President of Women in AI & Robotics. Sheila talks about the network she built in Germany, dedicated to fostering gender-inclusive, ethical, and responsible AI and robotics solutions. She highlights the importance of creating such a network and the positive impact it has had on the AI and robotics community.

Using AI & Machine Learning to Develop Better Healthcare Experiences with Sumayah Rahman and Vaibhav Verdhan Jun 24, 2024

In this episode of the Data Science Salon Podcast, host Anna Anisin sits down with two leading experts in the ML/AI healthcare industry. First, Sumayah Rahman, Director of Data Science - Machine Learning and Infrastructure at Cedar, discusses optimizing the patient experience to make healthcare more affordable and accessible. She explains how ML-powered discounts can benefit both patients and providers, sharing practical examples of using data to enhance patient experiences and highlighting the transformative impact of AI/ML in healthcare. Next, Vaibhav Verdhan, Analytics Leader at AstraZeneca, dives into the role of computer vision in healthcare and his favorite technologies in the healthcare analytics space. He discusses how advanced analytics are driving innovation at AstraZeneca by developing, deploying, and maintaining decision support capabilities. Both guests provide valuable insights into how AI and ML are revolutionizing healthcare, offering listeners practical knowledge and inspiration.

Lessons Learned from Applying Data Science in Finance and a Deep Dive into Drift with Mabu Manaileng and Adam Lieberman Jun 17, 2024

In this episode, Anna sits down with two leaders in the finance industry, exploring the forefront of AI and ML innovations. First, we have Mabu Manaileng, Lead Data Scientist at Standard Bank Group. Mabu shares his journey and current role, highlights the challenges of applied data science in the financial sector, and discusses the transformative impact of AI on banking in the coming years. Next, we welcome Adam Lieberman, Head of AI and ML at Finastra. Adam defines the concept of drift, discusses statistical measures to quantify it, and provides strategies for maintaining model health, ensuring that models continue to serve users' needs effectively.

Leveraging Statistical Models and ESG to Grow Your Business with Laura Gabrysiak and Rochelle March Jun 10, 2024

In this episode, Anna sits down with two leaders in the finance industry, exploring the forefront of AI, ML, and ESG innovations. First, let's welcome Laura Gabrysiak, Data Science Leader at Visa. Laura develops statistical models and decision analytics tools that enable Visa clients to transform massive amounts of data into actionable ML models and AI implementations. She's also passionate about fostering the local data science community in Miami as the Founder of R-Ladies Miami. In this conversation, they dive into the future of ML/AI in financial services and the impactful work being done with Code Art to promote diversity in tech. Next, we have Rochelle March, former Head of ESG Product at Dun & Bradstreet. Rochelle specializes in impact analysis related to carbon, water, and the Sustainable Development Goals, and applies machine learning to ESG products. She also teaches data and analytics at Bard College’s MBA program, sits on the advisory board for USL Technology, Inc., and mentors fellows in the Environmental Defense Fund’s Climate Corps program. Since recording this episode, Rochelle has started her own company, People Places Words Actions. In our discussion, we explore her journey in ESG innovation and analytics, why ESG data is crucial for responsible investment decisions, and how it drives sustainable business practices. Tune in to learn from these industry thought leaders and gain insights into the cutting-edge applications of AI and ESG data in the finance sector.

FinTech Insights: AI Innovations, Privacy Strategies, and Synthetic Data with Harry Mendell & Supreet Kaur Jun 03, 2024

In this episode, Anna sits down with two distinguished leaders in the ML/AI finance industry. First, we have Harry Mendell, Technology Group Data Architect at the Federal Reserve Bank of New York, who brings over 30 years of expertise in FinTech. Harry shares compelling stories and discusses emerging trends in the finance sector. Following Harry, Supreet Kaur, AVP at Morgan Stanley and product owner for various AI products, joins the conversation. Supreet provides insights into the use of synthetic data to protect customer privacy in FinTech, ensuring informed decision-making. This deep dive into synthetic data highlights its growing importance in the industry.

Context Matters: Generative AI, the spectrum of worldviews, and understanding propaganda's appeal Oct 24, 2023

Ben Dubow studied the Middle East during his undergrad and took a job tracking terrorist groups. After a brief stint at a large tech company, he launched Omelas, a company that combines AI and subject matter expertise to deliver intelligence to national security professionals.In today's episode, our Senior Content Advisor Q McCallum caught up with Ben to learn more about what Omelas is up to and how the company applies AI and data analysis to its mission.Along the way they explore the value of data in context; why it's important to ask the right questions of the right data, and not just the whole pool; the power of involving humans in the data pipeline; and what it takes to do NLP and NER at scale. The two also talk about the impact of generative AI on democracy and authoritarianism. A topic which, interestingly enough, holds lessons for corporations that plan to release AI chatbots.Links mentioned in this episode:

Ben's LinkedIn profile
Omelas website
Ben's writing on the Center for European Policy Analysis (CEPA) website
Article in Les Echos describing the project "Le Monde in English": "« Le Monde » parie sur l'étranger pour stimuler sa croissance"
Q's write-up on "Risk Management for Generative AI Bots" is available on both his O'Reilly Radar page and his blog.

When companies try to "sprinkle some AI" on a product May 17, 2023

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.

Noelle's Data Council talk, "Hot Takes and Tragic Mistakes: How (not) to Integrate Data People in Your App Dev Team Workflows"
Find Noelle on LinkedIn: https://www.linkedin.com/in/noellesio/
Q's blog post (which came out much better thanks to Noelle's help): "AI isn't something you just add to a company"

Building data products with Solomon Kahn Mar 07, 2023

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.

Solomon is active on LinkedIn. You can follow him for his daily updates at https://www.linkedin.com/in/solomonkahn
Delivery Layer: https://www.deliverylayer.com/

Probabilistic Thinking with James "JD" Long Oct 26, 2022

In this episode, James and Q explore:

The ideas of risk and uncertainty.
What is "probabilistic thinking" and why is it important for data scientists?
The career progression of a data analyst, and what it means to develop statistical acumen.
Thinking in terms of distributions, and thinking in different moments of a distribution.
Seeing BI, AI, and simulation in terms of punctuation. (No, seriously.)
How to bridge the gap into thinking probabilistically

And, just a reminder: James only speaks for himself in this episode and he does not represent his employer.Links mentioned during our discussion:

You can find James on Twitter at @cmastication and on LinkedIn at https://www.linkedin.com/in/jamesdlong/
R Cookbook, 2nd Edition (which James co-authored)
RenRe's open roles: https://bit.ly/renrejobs
Q's blog posts on "punctuation in data": Periods and Question Marks (BI and AI), and then Ellipses (simulation)

The list of books James mentioned:

Thinking in Bets (Annie Duke)
Fortune's Formula (Poundstone)
The Lady Tasting Tea (Salsburg)
Fooled by Randomness (Taleb)

The roles of economists in data science, with Dr. Amar Natt Aug 17, 2022

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.) You can find Amar online:

LinkedIn: https://www.linkedin.com/in/amarita-natt-ph-d-79028313/
Econ One Research: https://www.econone.com/staff-member/amarita-natt/

Be part of the conversation and connect with the data science community at DSS Miami Hybrid on September 21, 2022.

Book your ticket now.

ML at The Home Depot with Pat Woowong: The Falloff Model and Lead Scoring Jul 20, 2022

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.Other places to find Pat:

LinkedIn: https://www.linkedin.com/in/patwoowong/
"How THD keeps shelves stocked using ML" (the talk he mentioned during our interview): https://twimlai.com/podcast/twimlai/how-ml-keeps-shelves-stocked-home-depot-pat-woowong/
"The Value Proposition for Using ML in Brick-and-Mortar Retail Stores: Home Depot" https://www.youtube.com/watch?v=rF8jtdX-hGo

Be part of the conversation and connect with the data science community at DSS Miami Hybrid on September 21, 2022.

Book your ticket now.

Coffee Chat: Inspiring ML Use Cases in Retail Delivering Measurable Impact May 26, 2022

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.

Data Science and Data Engineering in the Federal Space with Dr. Pragyansmita Nayak May 19, 2022

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!" ?You can find Pragyan online:

Twitter: https://twitter.com/SorishaPragyan
LinkedIn: http://linkedin.com/in/pragyansmita

The book Q mentioned is Army of None, by Paul Scharre.

Software Development Skills in ML/AI May 05, 2022

In this episode, our Senior Content Advisor Q McCallum met up with Murium Iqbal from Etsy. They spoke about an important skill for data scientists: software development! 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.

Coffee Chat: Model Interpretability And How To Create Trust In AI Products Apr 27, 2022

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 Mar 02, 2022

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 (2/2) Jan 13, 2022

In the previous episode, our Senior Content Advisor Q McCallum met with product manager Chris Butler to explore the role of uncertainty and how it relates to AI product management. That conversation sets the stage for Chris and Q to talk about communal computing today.

Chris starts by explaining what shared, AI-backed devices mean for data collection, analysis, and regulation. After that, Chris and Q explore important questions such as: What are some challenges in getting communal computing devices to coordinate? How do social norms mix with assumptions made by the ML models behind these devices? What do we lose when we use data lakes? How do product managers and machine learning engineers interact on these kinds of projects? What do communal computing devices have in common with software developers on shared platforms?And, most importantly: what does all of this have to do with the film Napoleon Dynamite ...?

Chris has published a series of articles on communal computing: Communal Computing intro, Communal Computing’s Many Problems, and A Way Forward with Communal Computing.
You can also watch some of Chris’s communal computing talks:
AIxDesign Communal Computing workshop with animistic design mapping
Bots and AI Meetup - Communal Computing - Solving multi-user Alexa and Google Assistant use cases

Coffee Chat: DSS Virtual Finance & Technology 2021 Dec 16, 2021

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.

AI, Product, and Uncertainty with Chris Butler (1/2) Nov 04, 2021

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

Coffee Chat: DSSe Virtual 2021 Sep 16, 2021

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.

Analytics vs. Data Science vs. ML Research: Economist Sonali Syngal Shares Her View May 27, 2021

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

Charting a Course: from Physics PhD to Professional Data Scientist with Dr Resham Sarkar Apr 13, 2021

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.

Data Monetization Strategies with Micheline Casey Mar 25, 2021

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

Matt Godbolt: Software Testing, Performance Tuning, and Code Handoff for Data Scientists Mar 09, 2021

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

Coffee Chat at DSSVirtual for Healthcare, Finance & Technology Feb 19, 2021

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 Feb 02, 2021

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.

Coffee Chat at DSSVirtual for Finance & Technology Dec 14, 2020

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.

Coffee Chat from DSS Virtual - Retail & E-Commerce Nov 20, 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

Shane Glynn on Automated Content Moderation and the Intersection of AI and Law Nov 09, 2020

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.

Coffee Chat from DSS Virtual - Media, Advertising, & Entertainment Sep 30, 2020

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 Sep 16, 2020

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 Sep 02, 2020

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.

Jean-Georges Perrin on Spark and Data Quality Aug 05, 2020

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 Oct 21, 2019

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 Jul 12, 2019

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

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