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Building 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.
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By Dimitri Bianco5
66 ratings
Send us a text
Building 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

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