Chandra V

About Chandra V

Data Scientist with 8 years of experience in transforming business requirements into analytical models, designing algorithms, building models, developing data mining and reporting solutions that scale across a massive volume of structured and unstructured data and expertise working in a variety of industries including Banking and Healthcare. Expert in Data Science process life cycle: Data Acquisition, Data Preparation, Modeling (Feature Engineering, Model Evaluation) and Deployment.Skills:Business Intelligence: BASE SAS, Business Objects, Google Analytics, Advanced Excel (VBA, Vlookup, PowerPivot), Anaconda (Jupyter, Spyder)Programming: Python (Numpy, Pandas, SciPy), SAS, R, Scala, PHP, HTML, CSS, JavaScript,java, SQL, PostgreSQL, Scikit, Shell ScriptBig Data Frameworks: Pig, Flume, Sqoop, Hive, Hadoop, Spark, KafkaMachine Learning Algorithms: Naïve Bayes, Random Forest, KNN, K-Means, Decision TreePredictive Models: Logistic and linear regression, Time Series(ARIMA), Multinomial Model, PCA and Factor analysis, Conjoint AnalysisData warehouse: ER Modelling, Dimensional Modelling, Business Objects (IDT, UDT, Web Intelligence - Webi), SSAS (including DMX)Data Visualization Tools: Tableau, D3.Js, Seaborn, R-ggplot2, Power BI

Experience

1. Data Scientist

   AT&T

   Jul 2018 — Present

   Extensively involved in all phases of data acquisition, data collection, data cleaning, model development, model validation and visualization to deliver data science solutions. Built machine learning models to identify whether a user is legitimate using real-time data analysis and prevent fraudulent transactions using the history of customer transactions with supervised learning. Extracted data from SQL Server Database, copied into HDFS File system and used Hadoop tools such as Hive and Pig Latin to retrieve the data required for building models. Performed data cleaning including transforming variables and dealing with missing value and ensured data quality, consistency, integrity using Pandas, NumPy. Tackled highly imbalanced Fraud dataset using sampling techniques like under sampling and oversampling with SMOTE (Synthetic Minority Over-Sampling Technique) using Python Scikit-learn. Utilized PCA, t-SNE and other feature engineering techniques to reduce the high dimensional data, applied feature scaling, handled categorical attributes using one hot encoder of scikit-learn library Developed various machine learning models such as Logistic regression, KNN, and Gradient Boosting with Pandas, NumPy, Seaborn, Matplotlib, Scikit-learn in Python. Worked on Amazon Web Services (AWS) cloud services to do machine learning on big data. Experimented with Ensemble methods to increase the accuracy of the training model with different Bagging and Boosting methods and deployed the model on AWS. Experience in Implementing Continuous Integration and deployment using various CI Tools Jenkins, Docker & Kubernetes,monitoring EC2 instances, several AWS services using Nagios as well as log monitoring tools Splunk. Visualized the data with graphs and reports using matplotlib, seaborn and panda packages in python on datasets for analytical models to know the missing values, correlation between the features and outliers.

Education

• Andhra University

  Bachelor's degree, Computer Science

• University of Missouri-Kansas City

  Master's degree, Computer Science