Listen "Quant Pipeline"
Episode Synopsis
Send us a textBuilding a quant team, a data science team, or just an analytics team is challenging. Online there are many stories of why data science teams fail however all roles that build models or predict values using data run into similar issues. Getting a full pipeline from data to results requires a lot of pieces including data quality, training, hiring, external education, and process support. It takes more than a rockstar quant to get everything put together and running. Many teams fail because of the pieces is missing or not developed which could be due to a lack of resources or just not knowing they need it.Support the show
More episodes of the podcast Talking Tuesdays with Fancy Quant
Physicist to Quant and AI - Igor Halperin
21/10/2025
Alpha Engineer - Jeff Ryan
23/09/2025
NanoConda Founder - Roman Bansal
26/08/2025
Quant Insider - Tribhuvan
19/08/2025
OVVO Labs and Fred Viole
05/08/2025
Project Phoenix
29/07/2025
Mathematician and Quant - Raphael Douady
22/07/2025
Data Bento and Market Data
15/07/2025
ZARZA We are Zarza, the prestigious firm behind major projects in information technology.