Why Data Science Projects Fall Short of Expectations, And What to Do About it?

Considerations to hire & train or contract out data science talent.

Enterprises know just how hard it is to hire data scientists, taking greater than two months on average to fill roles, and paying well into the six figures for most practitioners. While these data scientists usually prove effective as analysts, the majority of the time they struggle to deploy their models due to a lack of software development skills and an inexperience with cloud platforms. As a result, the onboarding necessary to prepare data scientists to contribute to engineering organizations can take many months.

This problem leaves enterprises looking to build smarter products with two options: 1) come up with customized, thoughtful best practices for hiring and mentoring new data scientists in order to cut down time-to-productivity, or 2) contracting with an external service provider that has data scientists already trained on how to properly deploy and monitor their models. In this webinar, we go deeper into how to train data scientists to be ready to push to prod and how to decide whether building a upskilling program or working with an outside development organization is the best choice for organizations looking to incorporate greater intelligence into their products.

You will learn:

• Why data scientists often fail to contribute to engineering organizations
• How to train data scientists for production-ready delivery
• How to decide whether to hire and train data scientists or to work with external service providers

Presentations by:

Jonah Blumstein

Data Engineer & Data Scientist
Caserta

Jonah Blumstein is a Data Engineer and Data Scientist for Caserta. As an analytics practitioner, he works with clients across industries to deliver software products and statistical analyses that address key business problems. He previously spent five years at IBM in data science and strategy roles, and has an MBA with concentrations in Business Analytics and Management of Technology & Operations from the NYU Stern School of Business. He is knowledgeable in Python, SQL, R, and AWS; with additional experience in Py(Spark), Flask, Javascript, HTML, and CSS.