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Senior Applied Scientist | Machine Learning
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
University of Southampton
Doctor of Philosophy (PhD), Computer Software Engineering
January 1, 2009 – January 1, 2013
University of York
Master of Science (M.Sc.), Computer Software Engineering
January 1, 2008 – January 1, 2009
University of Shahid Beheshti
Bachelor of Engineering (B.Eng.), Computer Software Engineering
January 1, 2004 – January 1, 2008
GIG
Staff AI/ML engineer
April 1, 2023 – Present
Mountain View, California, United States · Remote
Microsoft
Senior Data & Applied Scientist
March 1, 2020 – April 1, 2023
Redmond, Washington
Capital One
Lead Machine Learning Engineer
May 1, 2017 – March 1, 2020
Mclean, Virginia
Comcast
Data Engineer
February 1, 2016 – May 1, 2017
Washington D.C. Metro Area
Accenture
Software Engineer
July 1, 2014 – February 1, 2016
Washington D.C. Metro Area
University of Southampton
Researcher
October 1, 2009 – June 1, 2013
Southampton, United Kingdom
DEPLOY
October 1, 2009 – Present
The overall aim of the EC Information and Communication Technologies FP7 DEPLOY Project is to make major advances in engineering methods for dependable systems through the deployment of formal engineering methods. Formal engineering methods enable greater mastery of complexity than found in traditional software engineering processes. It is the central role played by mechanically-analysed formal models throughout the system development flow that enables mastery of complexity. As well as leading to big improvements in system dependability, greater mastery of complexity also leads to greater productivity by reducing the expensive test-debug-rework cycle and by facilitating increased reuse of software. The work of the project will be driven by the tasks of achieving and evaluating industrial take-up, initially by DEPLOY's industrial partners, of DEPLOY's methods and tools, together with the necessary further research on methods and tools.
Cultural Fit Analysis
The candidate has a diverse background spanning fintech (GIG, Capital One), cloud services (Microsoft Azure), media (Comcast), and consulting (Accenture). This breadth of industry exposure suggests adaptability and a willingness to work in varied environments. The academic background with a PhD and research involvement indicates a strong inclination towards continuous learning and tackling complex, research-heavy problems, which aligns well with an ML Engineer role that often requires staying abreast of cutting-edge techniques. However, the project descriptions are primarily focused on technical achievements rather than team collaboration or cultural contributions, making a deeper assessment of cultural fit challenging without interview data.
Soft Skills & Operational Fit
The candidate's experience at Microsoft as a Senior Data & Applied Scientist, involving client consultation and training, suggests strong communication and collaboration skills. Leading a team at GIG also indicates leadership and project management capabilities. The focus on reducing default rates and improving customer support automation points to a results-oriented and problem-solving mindset.