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Work Authorization

Permanent Resident Of Canada


Machine Learning

Machine Vision ( CNN and OpenCV), Sequential Models ( LSTM), Linear and Logistic Regression, multivariate analysis, Segmentation and clustering, probability concepts, inferential statistics and statistical sampling techniques including hypothesis testing (t-tests), contingency tables and Chi-square analysis.


Pandas, Numpy, Keras, Tensorflow, Caffe, Torch , OpenCV, Sklearn, XGBoost

Databases / Applications

SQL Server, Oracle, AWS Athena, Google BigQuery

Other Languages

R, SAS, C++, Tableau

Work experience

Sep 2017Present

Senior Data Scientist - Deep Learning Lead 

Opta Information Intelligence
  • Developed Machine Vision and object detection models to predict the floor area of a house based on Google Street view and Google Satellite View (Tensorflow)
  • Lead a team of three on building various deep learning models on Machine Vision and NLP
  • Configure cloud infrastructure for Deep Learning, including setting up GPU instances on AWS and Google Cloud with Tensorflow, Keras, Torch, and OpenCV
  • Build Chatbots using DialogFlow for the claims division at SCM Insurance (Opta Parent)
  • Develop Flask applications to deploy Machine Vision and NLP models into production
Apr 2018July 2018

Student - Computer Vision Nano Degree

Udacity Inc. 
  • Automated Shopping Model: Created an Action Detection, Object Detection and Object tracking (TF, OpenCV, and Keras) based automated shopping model (Think Amazon Go)
  • Facial Keypoint Detection System: Built a facial keypoint detection system that takes in an image with faces, and predicts the location of 68  key points on each face
  • Image Captioning Project: Trained a CNN-RNN model to build a complex CNN-RNN image captioning model 
  • Landmark detection and Robot Tracking (SLAM): Implement SLAM for Robot localization and tracking in a 2D world using Kalman Filters
Nov 2017Mar 2018

Student - Deep Learning NanoDegree

Udacity Inc.
  • Bike Share Prediction: Built a neural network from scratch and implemented an MLP, i.e backpropagation and forward propagation using Python
  • Fashion MNIST Dataset: Developed a Keras based CNN to create a simple clothes classifier that uses the Fashion MNIST dataset. 
  • Dog Breed Classifier: Used Keras and Tensorflow based Convolutional Neural Network to implement a multi-class dog breed classifier. Also used OpenCV based Harr Feature classifiers to detect human faces and understand what breed of dogs they look like
  • Generate Simpsons TV Script: Built a recurrent neural network on TensorFlow to process text and then used it to generate completely new episodes of Simpsons, based on old scripts.
  • Generate Human Faces: Built a pair of Multi-Layer Neural Networks and made them compete against each other in order to generate realistic faces, using a Deep Convolutional Generative Adversarial Network implemented in Tensorflow
Oct 2016Sep 2017

Data Scientist - Contractor

Bank of Montreal
  • Build Anti Money Laundering models to isolate laundering and human trafficking behaviours using transactional Data
  • Created LSTM based RNN models in Tensorflow and Keras to uncover long term patterns in layering fraud
  •  Developed a series of models, from Linear GLM, unsupervised SVM to Deep Learning to segregate, identify risky customers and flag potential money launderers
Jun 2016Oct 2016

Data Scientist

TELUS, Toronto, ON
  • Use BASE SAS and Segmented Regression to build interrupted time series models that identify and quantify network upgrades because of Media Optimization
  • Created SAS scripts to do Variable clustering in order to reduce 850 Variables to less than a 100 important variables based on Information value
  • Built Logistic regression models in SAS to identify which past customers are most likely to sign up for TELUS business services if offered
  • Use R script to analyze Cell tower usage statistics and use dashboards  to share the information to upper management using R Shiny
  • Integrate Google Geocoding API in R to map Cell site performance statistics on an interactive map
  • Automated several data pulls from Oracle and Netezza using SAS macros, thereby reducing the time spent collecting data by over 70%
Apr 2013Sep 2015

Revenue Management and Pricing Optimization Quality Engineer

SAS Institute Inc, Cary, NC
  • Work for the IDEAS division at SAS, working on cutting edge statistical models for the Revenue Management and Hospitality industry
  • Support the development of advanced tools and analytics models using SAS BASE, SAS Macros and develop statistical reports to track, analyze, optimize and forecast revenues, revenue yields for large hotels in the US and around the world
  • Perform testing and validation of SAS and IDeaS Revenue Management Analytics algorithms and software.
  • Design analytic test programs by validating forecasting and optimization models using cutting edge algorithms. 
  • Use BASE SAS, SAS SQL, SDS SAS and Unix to create and automate test models for continuous monitoring of changes to algorithms
  • Develop parameterized solutions using SAS Macros, SAS SQL and batch processing to create a generic integration solution for testing of all hotel properties.
Apr 2010Apr 2013

FM Treasury Analyst III

BB&T Corporation, Winston Salem, NC
  • Supported treasury and other areas of the bank by utilizing SAS and other programming languages and building financial solutions for analysis and decision making. This included tax reporting processes, market risk, VAR, DV01, other risk reporting as needed, etc. 
  • Collected data from all lines of business as needed for balance sheet forecasting. I also conducted data integrity checks and developed data analysis to ensure accuracy of data and reporting. The reports were developed using BASE SAS, SAS Graph, SAS Macros and ODS
  • Built SAS forecasting models and conducted time series analysis to analyze trends and calculate credit risk by identifying future direction of swap spreads
  •  Develop analysis of the balance sheet assets or liabilities in relation to interest rate risk management, optimization of income, cash flow, price performance(price risk), securities transactions(bond swaps, financial derivatives, repurchase agreements etc.) and other specific security risk/return attributes.
Aug 2009Oct 2009

Sap Basis Administrator Intern

Coldwater Creek, Coeur D'Alene, ID
  • Responsible for SAP troubleshooting, performance monitoring, database administration and SAP user management
  • Named the“ SAP Basis Transport manager” for my role in importing SAP transports 


Apr 2018Jul 2018

Machine Vision Nano Degree

Udacity Inc.
Nov 2017Mar 2018

Deep Learning Nano Degree

Udacity Inc. 
Aug 2007May 2009

Masters in Management Information Systems

Oklahoma State University, Stillwater, OK

Aug 2003May 2007

Bachelors in Electrical Engineering

Osmania University, Hyderabad, India


May 2008Present

SAS Enterprise Miner 5.2 Predictive modeler 


This certification confirms that the candidate has a firm understanding and mastery of the functionalities for predictive modeling available in SAS Enterprise Miner 7.

Aug 2009Present

IBM Cognos 8 BI Author certification 


This certification confirms that the professional can build reports using relational data models, as well as enhance customize, and manage professional reports using IBM Cognos

May 2009Present

 OSU SAS Data Mining and Marketing Certificate

SAS and Oklahoma State University

The curriculum for this program was designed in partnership with SAS, a leading provider of business analytics and data mining software and services.

Other Continuing Education

  1. SAS Programming 1 (ECPRG1) : SAS Institute : Link
  2. Data Scientist's Tool box : Johns Hopkins University - Coursera Link
  3. R programming ( Johns Hopkins University - Coursera): Link
  4. Introduction to Statistical Concepts (ECSTAT0) : SAS Institute
  5. Introduction to Marketing : Wharton Business School and Coursera : Link
  6. Hadoop Fundamentals : Big Data University : Link
  7. Data Analysis Using R : Big Data University : Link