Pedram Navid is the Head of Data at Hightouch where he leads advocacy for data practitioners, community-building, and the internal data stack. Prior to Hightouch, he has worked as a data engineer building up a modern data stack from the ground up and as a data scientist where he helped scale analytics at a major bank. He contributes to open-source packages and creates data memes on Twitter.

In this episode, we cover a range of topics including:
- How he entered the world of data science
- Reverse ETL
- How can new data science professionals evaluate various roles in data science
- How can interviewees evaluate team culture
- How data gets political within a company
- How should data practitioners forge alliances
- What is data activation
- How he manages data teams

Amir Feizpour is the cofounder and CEO of Aggregate Intellect. It's a platform to accelerate knowledge discovery for research and development teams, including but not limited to AI. You can visit ai.science to learn more about it. Previously he worked in the industry as a data scientist, a senior manager, and a product lead in NLP. He has a PhD in Physics from University of Toronto and he did his postdoc in quantum computing at University of Oxford. You can join the Aggregate Intellect Slack community here. You can book a free 20-min coaching session with him here.

In this episode, we cover a range of topics including:
- His journey into the world of data
- What is knowledge discovery
- How to build online communities
- What he's building at Aggregate Intellect
- What does great data science culture look like
- What product has impressed you the most
- Current and future trends in AI

Margaretta Colangelo is the cofounder and CEO of Jthereum, an enterprise software company that enables Java developers to write smart contracts. She is also the President of U1 Technologies, an enterprise software company that provides the communications infrastructure for stock trading platforms used by the world's top investment banks. She serves on the advisory board of the AI Precision Health Institute at the University of Hawaii Cancer Center. She speaks at AI conferences around the globe and publishes a weekly newsletter on DeepTech with over 80,000 subscribers. She has published over 300 articles in Forbes, MIT Technology Review Italia, International Journal of Infectious Diseases, AI Time Journal, and many more. She is a San Francisco native and has built an amazing career over the last 30 years.

In this episode, we cover a range of topics including:

Journey into the world of AI:
- How did you enter the world of AI
- Learnings along the way

AI in healthcare:
- State of healthcare today
- How can AI benefit healthcare
- Milestones achieved in 2021
- AI for cancer research
- AI-powered drug discovery
- What's coming next

Career:
- How do you guide new professionals who want to do AI for healthcare? 
- How do you interview people? 

Trends:
- What product that you've personally used has impressed you the most?
- Compared to 5 years ago, what has been the biggest positive development in AI? 
- Where will AI be in 5 years?

Denis Rothman is an author of 5 books on AI and a prodigious creator of AI content. He is a speaker, inventor, designer, and a developer of AI solutions. He began his career authoring one of the first AI cognitive chatbots. He designed one of the very first word2matrix patented embedding and vectorizing systems. He was previously the cofounder of Planilog, a collaborative app for planning and scheduling your production and maintenance workflows. 

In this episode, we cover a range of topics including:

His journey into the world of AI:
- How did you enter the world of AI
- The role of mechanism
- In one of your articles, you talk about The Rise of Metahuman AI. What is metahuman AI? And why is it relevant?
- How to implement sustainable, ethical, and profitable AI?

Career:
- The role of mentors vs mavericks
- How should new AI professionals evaluate what to pursue?
- How do you hire people?
- Why is practice important in machine learning?
- How can basic mathematics can speed up your learning process?

Culture:
- How to leverage extreme programming to drive success?
- How to manage customers?
- How does he coach new professionals?
- How to build products under customer's infrastructure constraints?

Overall trends:
- What product has impressed you the most?
- His insight on why transformers have become popular
- What has been the biggest positive development in AI over the last 5 years?
- Where will AI be in 5 years?

Andrew is a data science educator at Lighthouse Labs. He teaches data science, coaches aspiring data scientists, and designs courses. He has worked with over 100 students from various backgrounds aiming to transition into data science.  

In this episode, we cover a range of topics including:

Andrew's journey into the world of data science:
- How he entered the world of data science
- His learnings from teaching over 100 students

Career:
- How can new professionals evaluate what area they like within data science?
- How should a data scientist look for jobs?
- How do you interview people? 
- How can a data scientist succeed at a new job?

Culture:
- How should data science professionals talk to customers?
- What does good data science culture look like?

Trends:
- What product has impressed you the most? And why?
- What has been the biggest change in data science compared to 5 years ago? 
- Where will data science be in 5 years?

Emilie Schario is a Data Strategist-in-residence at Amplify Partners. Previously, she was the Director of Data at Netlify, where she led 8% of the company's headcount, and was the first data analyst at many companies, including GitLab, Doist, and Smile Direct Club.

In this episode, we cover a range of topics including:

Emilie's journey into the world of data science:
- How she entered the world of data science
- Her learnings along the way
- Why Locally Optimistic is her favorite data community

Careers, Jobs, and Interviews:
- How can new professionals evaluate what area they like within data science
- How should a data scientist look for jobs?
- How do you interview people?
- What are some of the red flags during hiring?
- What should a data scientist do during the first 30 days of the job?

Culture:
- How should data science professionals talk to customers?
- What does good data science culture look like?
- How should first time managers think about imparting culture?

Current and future trends:
- What's your favorite resource for data science? And why?
- What has been the biggest positive development in ML compared to 5 years ago? 
- Looking forward, what aspect of ML excites you the most?

Mihail Eric is the founder of Pametan Data Innovation, a machine learning consultancy focused on helping organizations build data-driven systems to solve their toughest business problems. He's also the founder of Confetti AI, the premier educational platform for practitioners learning the skills to succeed in their machine learning and data science careers. His career has spanned machine learning industry, research, and engineering across domains such as conversational AI and self-driving vehicles. He has published papers at some of the world's top conferences including ACL, AAAI, and NeurIPS. He has helped start teams at innovative companies like RideOS and Amazon Alexa. Systems that he has architected are used by hundreds of thousands of people globally. He actively blogs and speaks about machine learning. And can be reached on Twitter and LinkedIn.

In this episode, we cover a range of topics including:

Mihail's journey into the world of machine learning:
- How he entered the world of machine learning
- His learnings from running a machine learning consultancy firm
- The role of educational platform and his learnings from building Confetti AI

Careers, Jobs, and Interviews:
- How do you guide new professionals in evaluating what area they like within ML? 
- How should a machine learning professional look for jobs?
- How do you interview people? 
- What are some of the red flags during hiring?

Product:
- How should ML professionals talk to users/customers?
- How important is the ability to write production-level code?

Overall trends:
- What product that you've personally used has impressed you the most? And why?
- What has been the biggest positive development in ML compared to 5 years ago? 
- Looking forward, what aspect of ML excites you the most?

Alexey Grigorev is the founder of DataTalks.Club, one of the world's most popular data communities. He is the principal data scientist at OLX. He has written a few books on machine learning. His most recent book is Machine Learning Bookcamp, which is especially relevant to software engineers who want to get into machine learning.

In today's episode we cover a range of topics including:

Personal journey into the world of data science:
- How did you enter the world of data science?
- Can you talk about your recent book Machine Learning Bookcamp? 
- You run one of the best data communities in the world. How did you launch it and get the first few members to join?
- What's the hardest part about running a community?

Careers, Jobs, and Interviews:
- How do you guide new professionals in evaluating what area they like within ML? 
- How should a data scientist look for jobs?
- How do you interview people? 
- What are some of the red flags during hiring?
- How do you onboard a new hire?
- What does a great ML professional look like?

Product:
- How should ML professionals talk to users/customers?
- What do you believe makes for a great product experience?
- How important is the ability to write production-level code?

Overall trends:
- What product that you've personally used has impressed you the most? And why?
- What has been the biggest positive development in ML compared to 5 years ago? 
- Looking forward, what aspect of ML excites you the most?