Vedhapriya Raman
ML EngineerData Engineer/Scientist
Stealth Startup
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Data Engineer/Scientist
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Machine learning engineer working on developing cutting edge ML Algorithms for IOT devices.
The University of Texas at Austin
Master’s Degree, Software Engineering
January 1, 2015 – January 1, 2017
Shanmugha Arts, Science, Technology and Research Academy
Bachelor of Technology (BTech), Electrical and Electronics Engineering
January 1, 2005 – January 1, 2009
Stealth Startup
Data Engineering
October 1, 2023 – Present
Remote
SparkCognition
Data Scientist III
July 1, 2019 – December 1, 2022
SparkCognition
Data Scientist II
March 1, 2018 – June 1, 2019
SparkCognition
Machine Learning Engineer
July 1, 2017 – March 1, 2018
Rocket Software
Machine learning Engineer
May 1, 2016 – December 1, 2016
Austin, Texas Metropolitan Area
Tata Consultancy Services
DB2 Developer
June 1, 2012 – May 1, 2015
United States
Tata Consultancy Services
Software Developer
June 1, 2009 – May 1, 2012
United States
IBM DB2 Fundamentals
IBM
June 24, 2026 – Present
Cultural Fit Analysis
The candidate has worked in various roles from Software Developer to Data Scientist III and ML Engineer across different companies, including a stealth startup. This diversity in roles and company types suggests adaptability. The progression through ML-focused roles at SparkCognition indicates a sustained interest and growth in the field. The experience aligns well with a technical, problem-solving culture often found in ML engineering teams.
Soft Skills & Operational Fit
The candidate's resume indicates experience leading a team of 5 DB2 developers, suggesting leadership and collaboration skills. Descriptions of developing robust testing frameworks imply attention to detail and quality. However, without specific psychometric test results or interview data, a comprehensive assessment of soft skills and operational fit is limited.