Rakesh Ramesh is a data scientist with a Masters in Computer Science from USC. He has work experience as a software engineer at TEKSystems, where he built recommendation systems and analyzed clickstream data. His skills include Java, Python, SQL, Hadoop, Spark and machine learning tools like R and MATLAB. He has done projects involving music recommendation using ALS, anomaly detection using k-means clustering, and facial recognition using convolutional neural networks.

1. Rakesh Ramesh
Email - rakeshr@usc.edu  Phone - +1 (323) 847-4606  Website - www.rakeshr.com
#720W 27th St., Unit 4, Los Angeles CA-90007  LinkedIn - https://in.linkedin.com/in/rakesh-r
Github - https://github.com/RakeshRameshDS
Education
University of Southern California LA
Masters in Computer Science (Data Science) 2016 – 2018
CGPA - 4.0/4.0
Courses - Foundations of Artificial Intelligence, Database Systems, Applied Natural Language Processing, Analysis of Algorithms
National Institute of Engineering Mysore, India
Bachelor of Engineering (BE), Information Science 2013
CGPA - 9.467/10, Gold medallist for Exemplary Academic Performance
Programming Skills
Languages - Java, Python, Hive, SQL, Pig, C++, C. Statistical Tools - R, MATLAB.
Platforms - Windows, Linux Framework - Scikit-Learn, Hadoop, Spark.
Work Experience
TEKSystems Bangalore, India
Domain - B2B/EAI Integration, Big Data, Machine Learning Jul 2013 - Sep 2015
Designation - Software Engineer
Responsible for:
 Responsible for Hadoop architecture set-up, analysis and aggregation of production data.
 Built recommendation system for Ahold and SCEA (Sony Computer Entertainment America).
 Analysed click stream data for Anomaly detection.
 Created models which have been deployed for predictive analysis of region based sales and causal analysis for determining
contributing factors.
 Managed digital transformations and implemented new business designs for Electrolux and Oracle.
 Managed the technology infrastructure and the day-to-day activities of trading partner process, including partner
on boarding, migrations and exception management.
 EDI Upgrade to split architecture to improve performance and Performance Testing using GreenHat.
Projects
 Spark - Music recommender using ALS (Data source: UCI - Audioscrobbler).
 Spark - Anomaly detection in network traffic using K-means clustering.
 Kaggle - ‘Facial Keypoint Detections’ using Convolutional Neural Nets
 POI Identification in Enron dataset using AdaBoost (Data source: CMU)
 Game playing agent using Adversial Search.
 Runaway Robot - Virtual Robot which navigates through obstacles using Successive Localization and Mapping (SLAM)
 S.A.U.P (Student Assistance Utility Program) - Application designed to assist international students by suggesting places
in field of interest of the user using recommendation and clustering engines.
Certifications
 R Programming Certification Through Coursera; John Hopkins, 2016
 Machine Learning Certification Through Coursera; Stanford University, 2015
 Artificial Intelligence for Robotics Certification Through Udacity, 2015
 Big Data Hadoop Certification Through Edureka, 2015