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Software Engineer
- Worked on Neuralnets, LSTM and Perceptron deep learning methods - Developing scalable distributed applications based on MapReduce paradigm. - Statistical model building and predictive analytics - Excellent trouble shooting skills with hadoop cluster and hadoop based applications. - Research experience with machine learning, data mining and big data analytics. - Highly motivated team player with strong design skills. Languages : Java,R, Python,Hadoop,C++,Pregel Frameworks: Spark, Apache Flink, TensorFlow, Caffe, Keras
Louisiana State University
Doctor of Philosophy (Ph.D.), Computer Science
January 1, 2009 – January 1, 2013
Software Engineer
March 1, 2018 – Present
Mountain View, California, United States
Teradata Aster
Senior Software Engineer, Machine Learning Platform
August 1, 2014 – March 1, 2018
San Carlos
Teradata Aster
Intern
May 1, 2013 – August 1, 2013
San Francisco Bay Area
Center for Computation and Tech at Louisiana State University
Research Assistant
January 1, 2009 – August 1, 2014
IBM
Software Engineer
January 1, 2007 – January 1, 2009
Bengaluru, Karnataka, India
SLK
Software Engineer
July 1, 2004 – December 1, 2006
Bengaluru, Karnataka, India
Cultural Fit Analysis
The candidate's background is heavily skewed towards software engineering, distributed systems, and machine learning platform development, with a strong academic research component. While there is some exposure to data analysis (e.g., 'Modelling Trends based Yelp Review Data' using R and MongoDB), the primary experience is not directly in a pure Data Analyst role. The extensive experience in building ML platforms and scalable algorithms could be an asset, but the direct alignment with a 'Data Analyst' target role, which often emphasizes business intelligence, reporting, and specific analytical tools, appears limited. The candidate's profile suggests a strong fit for a Data Scientist or Machine Learning Engineer role rather than a traditional Data Analyst.
Soft Skills & Operational Fit
The candidate's resume highlights research and development roles, suggesting strong problem-solving and analytical skills. The detailed descriptions of research projects indicate an ability to work on complex, long-term initiatives. However, without psychometric or English test results, it's difficult to assess communication clarity, stress handling, or team collaboration directly. The focus on individual research and development might suggest a preference for deep technical work over broad team leadership, but this is an inference.