Who is the CEO of DataRobot?

Company name:
DataRobot
Company URL:
Last updated:
October 3, 2025
Email:
d****************.com
Email accuracy:
High

Debanjan Saha

Debanjan Saha is the Chief Executive Officer of DataRobot, having joined the company in February 2022 as President and Chief Operating Officer before becoming CEO in September 2022. He is responsible for the global expansion of DataRobot's AI platform, driving innovation and impact for customers worldwide. Under his leadership, DataRobot has focused on simplifying AI for organizations of all sizes and has introduced key strategic initiatives like AI Cloud Expansion. Debanjan Saha became CEO of DataRobot in September 2022, after serving as President and COO since February 2022. Prior to DataRobot, he was VP/GM of Data Analytics services at Google Cloud, overseeing significant growth and launching products like BigQuery Omni. At Amazon Web Services, he led database services, including the development and launch of Amazon Aurora, which became the fastest-growing service in AWS history. Saha is an IEEE Fellow and a Distinguished Member of the ACM, with over 50 patent applications. His career reflects a consistent drive to democratize AI and make advanced technology accessible for businesses.



Career Milestones

  • Joined DataRobot in February 2022 as President and COO, became CEO in September 2022.
  • Led the global expansion of DataRobot’s AI platform and introduced strategic initiatives like AI Cloud Expansion.
  • As VP/GM at Google Cloud, launched BigQuery Omni and oversaw major data analytics growth.
  • At AWS, led the development and launch of Amazon Aurora, the fastest-growing service in AWS history.
  • Holds over 50 patent applications and is recognized as an IEEE Fellow and ACM Distinguished Member.



About DataRobot

DataRobot, founded in 2012, is an AI platform company. They help data scientists, business analysts, and enterprises across various industries like finance and healthcare. Core features include automated machine learning (AutoML), model deployment, and monitoring. Benefits include faster AI development, improved decision-making, and increased productivity.

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