AI Loves Data Podcast

Latest Episodes:

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

Show notes

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)

Writer and Host: Anna Anisin

Produced, edited, and mixed by the Formulatedby Team

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

Show notes

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.

Writer and Host: Anna Anisin

Produced, edited, and mixed by the Formulatedby Team

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

Show notes

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.

Writer and Host: Anna Anisin

Produced, edited, and mixed by the Formulatedby Team

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

Show notes

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. Writer and Host: Anna Anisin Produced, edited, and mixed by the Formulatedby Team

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

Show notes

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.

Writer and Host: Anna Anisin

Produced, edited, and mixed by the Formulatedby Team

Software Development Skills in ML/AI May 05, 2022

Show notes

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. Writer and Host: Anna Anisin Produced, edited, and mixed by the Formulatedby Team

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

Show notes

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. Writer and Host: Anna Anisin Produced, edited, and mixed by the Formulatedby Team

Coffee Chat: DSS Hybrid Miami 2022 Mar 02, 2022

Show notes

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.

Writer and Host: Anna Anisin

Produced, edited, and mixed by the Formulatedby Team

Communal Computing and AI with Chris Butler (2/2) Jan 13, 2022

Show notes

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

Writer and Host: Anna Anisin

Produced, edited, and mixed by the Formulatedby Team

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

Show notes

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.

Writer and Host: Anna Anisin

Produced, edited, and mixed by the Formulatedby Team