Tushar Suresh
Big Data EngineerMachine Learning Engineer at Amazon
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 at Amazon
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
I have been a Research Intern and Developer at several top class institutes like eBay Research Labs, Carnegie Mellon University Pittsburgh, IIIT Hyderabad. Couple of years ago, I attained a Computer Science MS degree specializing in Natural Language Processing and Machine Learning from Columbia University, New York. I am particularly interested in developing applications in the field of Natural Language Processing, Information Retrieval and the Semantic Web.
Columbia University
Master of Science (M.S.), Computer Science
January 1, 2012 – January 1, 2013
National Institute of Technology Karnataka
Bachelor of Technology (B.Tech.), Information Technology
January 1, 2008 – January 1, 2012
VVS Sardar Patel PU College
Pre University Degree, Science
January 1, 2006 – January 1, 2008
Amazon
Machine Learning Engineer
October 1, 2016 – Present
Austin, Texas
NewsCred
Software Engineer
February 1, 2014 – October 1, 2016
Greater New York City Area
eBay Inc
Graduate Intern
June 1, 2013 – August 1, 2013
San Francisco Bay Area
Carnegie Mellon University
Student Intern
May 1, 2011 – July 1, 2011
Greater Pittsburgh Area
IIIT Hyderabad
Intern
December 1, 2010 – December 1, 2010
Hyderabad Area, India
IIIT Bangalore
Intern
May 1, 2010 – July 1, 2010
Bengaluru Area, India
Summer Internship Project
June 1, 2013 – August 1, 2013
Was brought on board for an ongoing project. Tasks involved : - Identifying word-classes and understanding association between words in language from the titles of the eBay inventory. - Analyzing and clustering user-behaviour data based on different similarity metrics.
Semantic Frame Induction
February 1, 2013 – May 1, 2013
Developed a system that learnt domain specific semantic frames or relations from a large corpus of news articles. The system integrated semantic web resources like Machine Linking and DBPedia in semantic frame learning.
Understanding Infant Colic
October 1, 2012 – April 1, 2013
Attempted to understand infant colic by using ML and NLP techniques. Electronic Health Records (EHR) was processed to first identify colic related events. Association Rule Learning was then carried out on these events to understand inter-dependencies.
IR projects
September 1, 2012 – December 1, 2012
Built an IR system and a movie review classifier as part of the Search Engine Technology course. Implemented using Python.
NLP projects
September 1, 2012 – December 1, 2012
Built a sentence POS parser, named entity tagger, implemented the EM algorithm, IBM Models 1 and 2 for Machine Translation as part of the Natural Language Processing course. Implemented using Python.
Application of Web Usage Logs To Improve Delivery Of Quality Information
October 1, 2011 – April 1, 2012
Utilized the concepts of Web Usage Mining to discover and classify traversal patterns from web access logs. In the first stage, a hierarchical structure of traversal paths of web users from different parts of the network was generated. Later, both user domain information and previous usage behaviour was used to implement a type of personalized mining using the established hierarchical structure.
How Users' Request Elicit Support In Online Health Communities
May 1, 2011 – July 1, 2011
Trained machine learning and regression models to identify the textual features in thread starter messages, of an online breast cancer support group, that could explain and predict the type of social support received by the users of the community. The results demonstrated that emotional/informational support is driven by usage of certain types of linguistic styles by the user who seeks support. This research was part of ONLINE SUPPORT, a joint project between LTI and HCII supported by an NSF grant.
Supporting Collaboration in Wikipedia Between Language Communities
December 1, 2010 – December 1, 2010
Identified inconsistencies in the infoboxes of Wikipedia articles on the same topic in two different languages, English and Hindi. In addition to directly comparing the text on English and corresponding translated non-English text, the approach took into consideration other language characteristics like homophones and synonyms. Nouns were tested for homophones by comparing their metaphone codes and words with the same sense were matched using synsets provided by WordNet.
Course Design Centre
May 1, 2010 – July 1, 2010
Developed a web-portal using a content management system (Drupal) to enable structured and scientific design of engineering courses.
Cultural Fit Analysis
The candidate has a strong background in research and development, particularly in NLP and ML, which aligns with innovative and data-driven environments. The project diversity, ranging from academic research to industry solutions, suggests adaptability. However, the target role is 'Big Data Engineer', and while there's relevant experience, the primary focus in many projects and roles leans heavily towards ML/NLP applications rather than core Big Data infrastructure engineering. This might indicate a slight misalignment if the role is purely infrastructure-focused, but a good fit if it involves data science/ML engineering on big data platforms.
Soft Skills & Operational Fit
The candidate's experience descriptions indicate leadership (led a back-end team) and problem-solving skills (stabilized a log management system, delivered search experience improvements). The project descriptions suggest an analytical and research-oriented mindset. However, specific soft skill assessments are not available.