Jitendra leads a team of Gartner analysts focused on the key challenges of Chief Data and Analytics Officers and the practical steps they take to overcome these challenges. The outputs of the research are case studies, tools, and templates that provide practical guidance in four areas of data and analytics: value generation, quality, algorithmic credibility, and talent. For a complete and up to date list of the research, please go to https://blogs.gartner.com/jitendra-subramanyam/chief-data-analytics-officer-research-publication-list/.
Prior to Gartner, Jitendra led a team of machine learning data scientists and engineers at Synaptiq AI, a boutique consulting firm specializing in advanced machine learning techniques. He has 18 years of experience working with Information Technology professionals in large organizations worldwide, providing research, consulting, benchmarking, and quantitative analysis. Jitendra has been an instructor at Harvard Extension School, teaching an introductory course on Machine Learning designed for business professionals.
At Synaptiq AI, Jitendra led engagements for a diverse group of clients in media, technology, healthcare, and government. Working closely with customers, he was responsible for scoping the machine learning solution, crafting the right performance measures (both business and technical), and working hands-on with small teams of data scientists to implement the machine learning solution. Jitendra's projects spanned using machine learning to extract content from PDFs, matching similar but differently described vehicles across multiple automobile catalogs, improving customer engagement via personalization, and developing graph-network anomaly-detection techniques to fight cybercrime.
Head of Machine Learning Solutions
Director, IT Strategy and Operations
Director, Worldwide Marketing
Data and Analytics Leaders
Data and Analytics Strategies
B.S. Electrical Engineering
Ph.D. Philosophy of Science (Foundations of Physics)
1Machine learning techniques and use cases.
2Intelligently sensing and shaping demand for data and analytics.
3Quantifying the business value of data and analytics products/services.
4Increasing data and analytics literacy for data science teams and for executives.
5Data & Analytics IT Score Next (Maturity Diagnostic)