Python Engineer

Karthik Sree Kanthan

+1-505-***-**** • ******************@*****.***

https://www.linkedin.com/in/karthik-sreekanthan/

Skills

Proficient: C++, C, Python, Linux, SQL, XML, Eclipse, Git, MATLAB, Haskell, SOAP UI, PySpark, Atlassian tools (Confluence, JIRA, Bitbucket), Pandas, UFT.

Exposure: C#, Java, React JavaScript, Visual Studio, HTML, CSS, Bootstrap, Selenium, Databricks, Apache Spark, Tensorflow, Keras, HP ALM.

Education

University of New Mexico 2018-2019

Pursued Master of Science in Computer Science GPA: 3.54 Graduate Coursework: Data Structure and Algorithms, Database Management, Software Foundations, Introduction to Cyber Security, Complex Adaptive Systems, Introduction to Machine Learning, Computer Networks, Introduction to Mobile Computing, Advanced Machine Learning, Bitcoins, Block Chains, Introduction to Big Data.

Amrita Vishwa Vidyapeetham 2011-2015

Pursued Bachelor of Science in Electronics and Communication Engineering GPA: 3.4 Work Experience

Project Assistant, University of New Mexico April 2020-Present

Volunteering for re-hauling of websites to maximize usability and developing formats utilizing XSLT/HTML/CSS so as to allow non-technical users to edit information easily using Cascade Management System (CMS). Software Engineer, Tata Consultancy Services September 2015 – February 2018

Worked in an agile environment on Web Service Testing for an application for an Insurance company, that provided insurances for Home, Auto, Renter and Condo. Made use of the Web Service Definition Language (WDSL) using XML in SOAP UI.

Worked on testing of an application using the Selenium Framework. Built the script using eclipse. Projects

Movie Recommender System 2019

Implemented the Collaborative filtering technique with ALS algorithm

Implemented split validation on the dataset and reduced the RMSE of the model by increasing the parameter for number of iterations and thus improving the predictions. PySpark DNA-level Splice Junction prediction 2018

Implemented ID3 Machine Learning Algorithm to classify DNA sequences

Improved the accuracy by 13% by tweaking the training process of the data by fair approximation of largely seen pattern of sequences. Python

Classification of Music Files 2018

Classified various music files as based on their genre

Made use of different data extraction technique, MFCC (Mel-frequency cepstral coefficients), apart from traditional method of Fast Fourier Transform (FFT) and Short Time Fourier Transform (STFT), to increase the accuracy by 8.5 %. Python

Automatic Document Classification techniques of Naïve Bayes and Logistic Regression 2018 Classified 12000 documents into 20 newsgroups

Statistically compared the efficiency of the two algorithms for our data set and chose Naïve Bayes as the final choice to improve initial normalization techniques, thus reducing the running time of the algorithm by 6 hours. Python

Contact this candidate