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Applied 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
Always exploring good opportunities to design and model with machine learning , which are explainable, maintainable and scalable.
Stanford University
Computer Science/SCPD grad. studies ( Information Retrieval, Machine Learning and NLP )
January 1, 2013 – Present
Southern Illinois University Edwardsville
Master of Science (MS)
January 1, 1993 – January 1, 1993
Osmania University
ME, Systems Engineering
January 1, 1990 – Present
Cardlytics
Applied ML
November 1, 2021 – Present
San Francisco Bay Area · Hybrid
Verizon Media Platform
Applied ML/DL for Ad. Platforms
January 1, 2013 – October 1, 2021
Sunnyvale CA
Buysight, Inc. (acquired by AOL, Inc. in 2012)
Applied ML for Ad. retargeting
July 1, 2011 – December 1, 2012
Yahoo
Applied ML for fraud detection
November 1, 2007 – June 1, 2011
Sunnyvale/Santa Clara CA
BroadSoft
ML/stats Engineering
January 1, 2004 – October 1, 2007
Cupertino / Sunnyvale
Nokia
VoiceXML/web Engineering
January 1, 1999 – January 1, 2004
Campbell CA
Agilent Technologies
R & D Engineer
January 1, 1996 – January 1, 1999
Palo Alto, CA
InfoGain Inc.
Senior Software Engineer / Service Line Manager
January 1, 1994 – January 1, 1996
Santa Clara CA
ECIL
Technical Officer
January 1, 1987 – January 1, 1992
Cultural Fit Analysis
The candidate has a long and diverse career path across various companies (Cardlytics, Verizon Media, Yahoo, Nokia, Agilent, etc.) and domains (AdTech, fraud detection, telecommunications, chemical analysis). This breadth of experience suggests adaptability and a willingness to engage with different organizational cultures and technical challenges. The continuous focus on ML/AI since 2007, with a strong alignment to the target role of ML Engineer, indicates a dedicated career trajectory. However, the lack of explicit project details or community involvement makes it difficult to fully assess cultural fit beyond professional adaptability.
Soft Skills & Operational Fit
The candidate's extensive experience in leading ML initiatives and guiding teams suggests strong leadership and collaboration skills. The descriptions of building 'open injectable algorithms' and 'custom adversarial trainers' imply a proactive and innovative approach to problem-solving. However, without specific psychometric test results or interview data, a detailed assessment of soft skills and operational fit is limited.