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Head of Engineering

Datatron Names Jason Revelle, Formerly From MasterCard, as Head of Engineering

Datatron, the pioneer of ModelOps and Model Governance platform is excited to announce that Jason Revelle is joining the company as Head of Engineering.  In this role, Jason will lead and scale Datatron’s engineering team. He brings many years of experience leading engineering teams at multiple companies including Mastercard.

Head of Engineering

Below, Jason shares his reasons and excitement for joining Datatron:

I am super excited to be joining the team at Datatron!

For the last ten years or so, I have spent a lot of time studying the practice of rules-based analysis and machine learning.  While others have focused on answering specific problems through these techniques in order to operate their complex, data-driven businesses, I have always been fascinated with a different question:  How can one fundamentally accelerate the solving of many problems, beyond the obvious infrastructure required?  How do you scale solutions to take advantage of AI and decision making using operationally sound and secure, yet low-code, agile approaches?

This is what made Datatron the perfect fit in my mind to take this mission to the next level.  During the past few years, I have obsessed over how to achieve better collaboration between data scientists, dev ops, and data engineering to simplify the back-and-forth, provide easier means to deploy and monitor growing numbers of models, while at the same time not locking these individuals into over-simplified workbenching or other limiting technology decisions which stifle their innovation.

“I see the opportunities in our products at Datatron as massive and could not be more passionate about our future.”

I look forward to meeting the full crew at Datatron, as well as getting to know all our customers!

We, at Datatron, provide an enterprise-grade platform that helps you to supervise your Machine Learning models for high precision deployment to meet the regulatory requirements and effective management of the entire production machine learning life cycle.

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