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Applied Machine Learning @ Microsoft
My expertise is transforming businesses with state of the art machine learning technology (inc. deep learning). I have delivered internationally recognised solutions in industry and research. Initially from a software development background, my solution architecture, machine learning and data science experience enable me to identify and deliver transformative business solutions. I have a successful track record delivering large enterprise solutions in the following functional areas: online retail (e-commerce), global product websites, defence, finance (retail and commodities), payment card processing, payment card fraud detection and system integration.
University of Hertfordshire
PhD Computer Science, Machine learning for image segmentation and classification of galaxies in Hubble images.
January 1, 2014 – January 1, 2018
Microsoft
Applied Machine Learning Scientist
June 1, 2018 – Present
London Area, United Kingdom
Pixel Incognita
Consultant Machine Learning Scientist
December 1, 2016 – December 1, 2017
London, United Kingdom
University of Hertfordshire
PhD - Machine Learning
September 1, 2014 – September 1, 2018
Hatfield
HP
Solutions Architect
September 1, 2010 – August 1, 2013
Bracknell, UK - Austin TX
Hewlett Packard
Software Architect
September 1, 2009 – October 1, 2010
Hewlett Packard
Global Payment & Fraud Management Technical Architect
August 1, 2006 – August 1, 2009
Cultural Fit Analysis
The candidate's experience is heavily skewed towards Machine Learning, Computer Vision, and prior architecture roles at large enterprises (HP, Microsoft). While the architectural experience is relevant, the deep specialization in ML/AI might not be a direct fit for a pure 'Backend Engineer' role without explicit backend development experience beyond architectural design. The diversity of projects is limited to ML/AI and enterprise architecture, which may not align with a general backend engineering culture.
Soft Skills & Operational Fit
The candidate's resume indicates a strong background in technical leadership and architectural roles, suggesting good problem-solving, decision-making, and potentially team collaboration skills. However, without specific assessment data, a definitive evaluation of soft skills and operational fit is limited.