Elite Team of Dedicated Data Scientists Advance the Journey to AI for more than 130 IBM Customers
IBM announced momentum and growth for its vaunted Data Science Elite Team, which has helped propel IBM to number one in global AI market share by revenue as ranked by IDC. In addition, the Elite Team has played a key role in helping close the data science skills gap, through unique academic programs. To date, the Data Science Elite Team has grown to almost 100 data scientists around the world and has been deployed on more than 130 Artificial Intelligence, Data Science and Machine Learning projects, advancing the journey to AI for global customers from Royal Bank of Scotland to Wunderman Thompson Data.
“Companies come to us because they know they need an AI plan, but they often don’t know where to start,” said Seth Dobrin, Phd, Vice President, IBM Data and AI, and CDO IBM Cloud and Cognitive Software. Dobrin helped architect and manage the Elite Team. “Today, the Elite Team has become a key plank of IBM’s AI strategy to help companies overcome the pitfalls of AI through innovative technologies and our dedicated Elite Team of data scientists.”
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IBM’s Data Science Elite Team tackles the challenges facing many organizations in the area of data science and AI – a skills shortage. In its Linkedin Workforce Report, Aug. 2018, Linkedin stated there were 151,000 unfilled data scientist jobs in the US Last week, in the firm’s new 2020 Emerging Jobs Report it ranked Artificial Intelligence Specialist and Data Scientist, #1 and #3, top jobs for 2020, respectively, showing annual growth of 74% and 37%.
The Data Science Elite Team has helped IBM drive Artificial Intelligence, Data Science and Machine Learning deeper into enterprises, one client at a time. But the effort has become a pillar of the company’s AI story and philosophy, to help clients overcome the challenges of AI adoption. IDC ranks IBM number one in global AI market share by revenue, with companies leveraging the Elite Team to apply Data Science and Machine Learning to rising data challenges. In the past year alone, the Data Science Elite Team has grown in size from 30 data scientists to almost 100 and grown its roster of client engagements to more than 130.
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“We were committed to moving toward a fully distributed architecture for our Machine Learning but one of the biggest challenges we faced was resources,” said Adam Woods, CTO at Wunderman Thompson Data. “Working with IBM and its Data Science Elite Team, our teams can now free up time from their day-to-day responsibilities to learn and build a proof of concept organically. Our data scientists focused on the immediate business requirements and the IBM team focused on the technology. This joint collaboration resulted in a machine learning pipeline, via Watson, that fully utilized all of our data signals to produce models that increase the performance over our previous models by 200% or more. We are now working aggressively to roll this out into our production.”
In addition to assisting clients in their AI and digital transformations, the Elite Team has been at the forefront of learning and development projects. This fall, IBM Elite Team members teamed with the Linux Group and University of Pennsylvania to create an open source curriculum program that colleges can use to fast-track actual data science curricula – free of charge.
IBM, the University of Pennsylvania, and the Linux Foundation are building an innovative, first-of-a-kind open source project to help enable universities around the world to build Data Science programs faster. With IBM’s involvement and industry expertise, University of Pennsylvania’s long-standing academic leadership and the Linux Foundation as a premier open source consortium, we are creating a curriculum kit comprised of a set of open source building blocks for teaching the core concepts of data science in undergraduate and graduate programs. These building blocks are based on Python and open source tools and frameworks, and include slides, documentation, code, and data sets that could be adopted or updated by users. This work is set to be available on Github in early 2020.
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