Meet DMV-area startup & patent-holding CTO building next generation low code data science platform

Salynt's Co-Founder and CTO, James Dempsey, aims to revolutionize technological innovation in the next stage of growth.

#datascience #tech #artificialintelligence #lowcode #softwareengineering

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James is the technological genius behind Salynt's low code data science platform (Coming Soon).

His deep-tech experience – with a background in computer and nuclear engineering, software architecture, and analytic development – complements the vision and direction of Salynt to accelerate AI adoption and automation.

CEO Jeremy Lawson commented, "James has an exceptional ability to take complex concepts and turn them into actionable products. His value to the team is paramount; James' visionary mindset will allow Salynt to produce new technology and further solidify our commitment to creating world-class solutions for the Data Science community."

Before Salynt, James began developing modeling and simulation software for ballistic missile radar tracking at MIT's Lincoln Lab. Since then, he's worked as a software engineer on the Real-Time Processing (RTP) team at Xandr, working on bid and impression process optimization in real-time and curated-deal advertising auctions.

As an Associate at Booz Allen Hamilton, James led the patented creation of a modular analytic development framework. He used this framework to construct multiple cyber analytics, which the 688th Information Operations Wing (IOW) used for network traffic classification.

James holds bachelor's degrees in electrical engineering and computer engineering and a master's degree in nuclear engineering from Purdue University.

To learn more about what James is developing, sign up today to demo Salynt's application.

About Salynt

Salynt's platform will prepare developers for current and future workplace challenges for implementing AI initiatives. Data Scientists will be able to rapidly produce automated analytics, which gives back time to Software Engineers to focus on more complex system operation tasks. Software Engineers can develop their machine learning engineering skills, and Data Scientists can learn to roll out ML models fit for production that benefit their company and their users.