SUMMARY:

• A Particle physics doctorate holder and a Data scientist/Research Scientist with overall Twelve years of experience in data analysis and Machine Learning using numerical and statistical approaches in both business and scientific industries.
• Strong background in statistical data analysis and numerical methods
• Strong background in manipulating and analyzing complex big data (Terabyte/Petabyte scale)
• Strong background in data simulation, data visualization and data modeling
• Hands on experience in developing physics analysis software tools using C++/C, Python
• Proficiency in statistical data analysis packages Python, R and ROOT (comparable to R)
• Hands on experience in Distributed File System (DFS) using GRID
• Hands on experience in text mining, and NLP
• Hands on experience and depth knowledge on Supervised and Unsupervised Machine learning techniques
• Working knowledge in analytical tool MATLAB/Octav and JAVA
• Hands on experience in MySql (SQL), postgresql and Hive/Hbase
• Hands on experience in Hadoop/Spark and Map - Reduce
• Depth theoretical knowledge of Machine Learning
• Hands-on experience in Machine Learning algorithm development (linear Regression) using C++ and Python (pandas, numpy, scipy)
• Hands on experience in Machine Learning Tools SCIKT, WEKA, TMVA, PySpark and R
• Hands-on experience with pixel sensor data analysis using ROOT (based on C++)
• Excellent knowledge of probability and statistical methods
• Excellent numerical, analytical and logical thinking skills
• Hands on experience in Kaggle.com Machine Learning competitions
• Hands on experience BI tools such as Looker Related Courses
• Machine Learning theory and Algorithm, Statistics, Statistical Mechanics, Solid State Electronics, Fiber Optics, Advanced Quantum Physics, and Electronics

PROFESSIONAL EXPERIENCE:

Confidential, San Jose, CA

Sr. Data Scientist

Responsibilities:

• Data mining and Data visualization using business and call logs
• Upsell customer prediction using Machine Learning methods
• User retention studies
• Customer Churn prediction using Machine Learning methods
• Translate data into business friendly visualization view using visualization tool Looker

Confidential, San Roman, CA

Data Scientist

Responsibilities:

• Data mining and Data visualization using business and sensor/GPS data
• Predict business decision models using Machine Learning method (random forest, svm, bayesian network, etc..) using both numerical and textual data (unstructured data)
• Use clusterization techniques for unsupervised text data
• Predict faulty reasons on GE locomotives (GE transportation) using Machine Learning techniques (random forest, svm, neural network, etc.) from both sensor and GPS data.
• Python (pandas, numpy, scipy, matplotlib, scikit, bayespy, fuzzywuzzy, ntlk) and R, GreenPlum database, spark/hadooop are being used in this project

Confidential, San Jose, CA

Data Scientist

Responsibilities:

• Multivariate performance prediction of switches and security hardware using Machine Leaning techniques from historical data using python (pandas, numpy, scipy, matplotlib, and scikit) and R
• Predict future hardware performance using Machine Learning techniques from historical and customer feedback data using python (pandas, numpy, scipy, matplotlib, and scikit) and R

Confidential, Santa Clara, CA

Data Analyst

Responsibilities:

• VMware and netapp storage performance analysis and prediction using Python (numpy, Matplotlib, scipy) and R
• Log analysis of storages (Netapp, Equalogic, compellent) and Linux/Unix servers
• Network traffic Analysis and Prediction

Confidential, San Jose, CA

Data Analyst

Responsibilities:

• VMware and Confidential storage performance analysis and prediction using Python (numpy, Matplotlib, scipy) and R
• Log analysis of storages (Netapp, Confidential ) and Linux/Unix servers
• Automation using shell (bash) scripts and python

Confidential

Research Scientist/Assistant

Responsibilities:

• Data analysis and data mining using ROOT (C++), Python, R and Java
• Upgrade and maintain physics simulating packages for ATLAS software. Code developed with C/C++ and Python
• Worked on data mining, modeling and analysis of complex, high-volume , high-dimensionality data with ROOT (C++) and Python
• Developed algorithms and numerical methods for investigating and measure of cut flow for Confidential, top cross section, and minimum bias analysis in ATLAS detector using ROOT (C++)
• Developed computer algorithms and strategies for ATLAS experiment. Code developed using C/C++ and Python with Linux shell scripting
• Used Multivariate Machine learning tools TMVA, scikit, and R for signal and background studies
• Developed linear regression machine learning algorithm and data modelling using ROOT (C++)
• Used GRID Distributed File System to run the jobs

Confidential

Research Assistant/Scientist

Responsibilities:

• Stationed at world’s premier Confidential research facility, Confidential, Geneva
• Worked on data mining, modeling and analysis of complex, high-volume , high-dimensionality data with ROOT (comparable to R) and Python
• Fully participated in building ATLAS pixel detector such as pixel sensor installation, opto-board installation, network connection, fiber connection, and installing back-end severs
• Data management (TB and PB scale) for data collected from the ATLAS detector for Minimum Bias and Confidential physics analysis
• Developed computer algorithms and strategies for ATLAS experiment. Code developed using C/C++ and Python with Linux shell scripting.
• Developed linear regression and classification machine learning algorithm
• Developed Physics Analysis packages for data analysis using ROOT and Athena packages (based on C/C++ and Python)
• Used Multivariate machine learning tools TMVA, scikit, and R for signal and background
• Expert support for the Receiver Hardware System of the ATLAS detector (Pixel detector)
• Involved in the first Confidential data (7 TeV) physics analysis in SuperSymmetry ( Confidential ) studies, and in first Confidential data physics analysis, in minimum bias measurements with ATLAS experiment
• Carried out pixel detector data analysis for detector tuning using ROOT and Python
• Used GRID Distributed File System to run the jobs

Confidential

Research Scientist/Assistant

Responsibilities:

• Developed algorithm for J/Ψ studies using D0 (at FERMI-LAB, Batavia, IL) experiment
• Developed algorithm for ttbar cross section studies using D0 (at FERMI-LAB, Batavia, IL) experiment
• Used Multivariate Machine learning tool TMVA, for signal and background studies for ttbar cross section
• Used GRID Distributed File System to run the jobs