Richard Freeman

Background Information

  • Current Role: Lead Data and Machine Learning Engineer, Data Scientist, and Cloud Architect in JustGiving, a tech for good company recently acquired by US Blackbaud, the world’s leading cloud software company powering social good for nonprofits.
  • Specialties: AWS and Azure Cloud Computing, Solution Architecture, Natural Language Processing, Serverless, Scalable Algorithms, Machine Learning, Graph Analytics, Neural Networks, Data Science, Streaming Analytics, Data Engineering
  • Technologies: Python, Spark, Serverless, Scala, Amazon Web Services, Azure, NoSQL, LINUX, SQL

Dr Richard T. Freeman graduated from the University of Manchester after studying a 4 year MEng in Computer Systems Engineering. He then successfully passed an EPSRC sponsored PhD in Machine Learning and Natural Language Processing at the University of Manchester under the supervision of Dr. Hujun Yin.

He then worked for six years in Capgemini a leading global IT services and consulting company as a Solution Architect, where he helped deliver large scale projects for Fortune Global 500 companies in insurance, retail banking, & financial services as well as a financial regulator and central government. He is now working in a JustGiving a tech for good company whose aim is to maximise funding for good causes and charities.

During his PhD and work in industry, he published a number of journal (including IEEE Transactions series) and conference papers, presented his research at international conferences, and shared his work at various IBM and Amazon events.

With a combination of artificial intelligence and natural language processing skills from his PhD and over 14+ years of experience as a data scientist, architect, designer and developer across the industry, he now wants to make a difference in the peer-to-peer crowdfunding and fundraising space. Richard has a passion of working on scalable and resilient cloud based data driven consumer products and data science platforms.

A list of his publications can be found on DBLP:Richard T. Freeman | or more extensive in Richard Freeman’s publications section.