Vincent is a Research Scientist Manager at the Central Applied Science Team at Meta. In this episode, Vincent provides insights into the unique role of economists in tech, particularly those who combine economic expertise with software engineering skills. We cover the challenges of hiring economists with the necessary technical abilities, the importance of clear communication in business settings, and the evolving landscape of forecasting and causal inference in tech.

Vincent discusses the team's work on forecasting and causal machine learning, highlighting the importance of balancing statistical accuracy with a structural understanding of business drivers. The conversation also touches on AI's potential impact on economists' work and its current limitations in replacing human expertise in complex analytical tasks.

Where to find Vincent
https://www.linkedin.com/in/vleahmar/

Where to find Ida
www.idajohnsson.com
https://www.linkedin.com/in/ida-johnsson/

Timestamps

00:00 Intro
04:31 What Makes Vincent's Team Unique
07:11 Is Observational Causal Inference Scalable?
09:54 Tools For Non-economists
12:07 Forecasting - Accuracy Vs. Explainability
16:30 Importance Of Feature Engineering Vs. Model Choice
17:40 How Are Economists Different From Other Scientists?
18:40 Why Vincent Struggles To Hire Economists
21:43 Skills Economists Tend To Lack
24:31 Causal Inference At Scale
27:11 The Importance Of An Entrepreneurial Mindset
28:35 Gap Between Academia And Private Sector
30:51 Who Works On Vincent's Team
32:24 Ideal Candidate For Vincent's Team
34:57 Communication Skills
39:41 Deep Learning
41:59 How Vincent Keeps Up With The Latest Research
43:40 Forecasting And Causality
47:44 DAGs And Do-calculus Vs. Potential Outcomes
49:47 Thoughts On AI