Environment: AWS RedShift, EC2, EMR, Hadoop Framework, S3, HDFS, Spark (Pyspark, MLlib, Spark SQL), Python 3.x (Scikit-Learn/Scipy/Numpy/Pandas/NLTK/Matplotlib/Seaborn), Tableau Desktop (9.x/10.x), Tableau Server (9.x/10.x), Machine Learning (Regressions, KNN, SVM, Decision Tree, Random Forest, XGboost, LightGBM, Collaborative filtering, Ensemble), NLP, Teradata, Git 2.x, Agile/SCRUM

Environment: ER Studio 9.7, MDM, GIT, Unix, Python (SciPy, Numpy, Pandas, StatsModel, Plotly), MySQL, Excel, Google Cloud Platform, Tableau 9.x, D3.js, SVM, Random Forests, Naïve Bayes Classifier, A/B experiment, Git 2.x, Agile/SCRUM., MLLib, SAS, regression, logistic regression, Hadoop, NoSQL, Teradata, OLTP, random forest, OLAP, HDFS, ODS, NLTK, SVM, JSON, XML, MapReduce

Applied Lean Six Sigma process improvement in plant and developed Capacity Calculation systems using purchase order tracking system and improvement inbound efficiency by 23.56%.Worked with Machine learning algorithms like Linear Regressions (linear, logistic etc.) SVMs, Decision trees for classification of groups and analyzing most significant variables such as FTE, Waiting times of purchase orders and Capacities available and applied process improvement techniques.In addition, calculated Process Cycle efficiency of 33.2% and identified value added and non-value added activities. Aggregate all available information about the customer. The data that is obtained for predicting the churn is classified in the following categories.Environment: SQL Server 2012, Jupyter, R 3.1.2, Python, MATLAB, SSRS, SSIS, SSAS, MongoDB, HBase, HDFS, Hive, Pig, Microsoft office, SQL Server Management Studio, Business Intelligence Development Studio, MS Access.

Developed Hive queries that compared new incoming data against historic data. Built tables in Hive to store large volumes of data.Used big data tools Spark (Sparksql, Mllib) to conduct the real time analysis of credit card fraud based on AWS Performed Data audit, QA of SAS code/projects and sense check of results.Environment: Spark, Hadoop, AWS, SAS Enterprise Guide, SAS/MACROS, SAS/ACCESS, SAS/STAT, SAS/SQL, ORACLE, MS-OFFICE, Python (scikit-learn, pandas, Numpy), Machine Learning (logistic regression, XGboost), Gradient Descent algorithm, Bayesian optimization, Tableau.