Hi there !! I’m currently working as a Senior Data and Analytics Engineer @Microsoft. As a part of Diag App Analytics I work on building the an end-to-end analytical solution to visualize the Customer and Outreach insights using PowerBI dashboards for diagnostic products by developing ETL pipelines using Azure Synapse Analytics and other Azure resources for secured connectivity. In my previous position as a Software Development Engineer at mPower Clinical Analytics team @Microsoft, I worked on building advance search capabilities of the mPower search on Radiology Reports and also extracting quality control measures (Critical Results, Actionable Findings, Followup Recommendations and Laterality Mismatches) from RAD Reports which needs an immediate communication to the ordering physician. As a Data Science intern at Channing Division of Network Medicine, Brigham and Women's Hospital, I have got an opportunity to collaborate with the physicians, bioinformaticians to design and implement various machine learning and deep learning approaches to classify COPD genomic data into cases/controls. I had a Masters in Data science from Northeastern University , Boston, MA. With an adept knowledge in statistics, machine learning , natural language processing, information retrieval . I'm interested in applying my statistical and analytical skills to tackle real world challenges. My Specialities: ETL, Data Warehousing, Dimensional Modeling, Data Visualization Data Cleaning, Predictive Modeling, Exploratory Data Analysis, Data Visualization, Text Mining, Time Series Forecasting, Machine Learning, Supervised and Unsupervised Learning, Regression and Classification algorithms, Regularization Techniques, Natural Language Processing, Deep learning, Sentiment Analysis, Hypothesis Testing, PCA, Market Basket Analysis My Skills set include Programming Languages: Python, R, Scala, SQL, C++, Java, MATLAB, Awk Databases: Oracle, MySQL, MongoDB, Neo4J Machine Learning: Linear/Logistic Regression, SVM, Tree Based, Neural Networks, Clustering ML Tools: Scikit Learn, Pandas, Numpy, PySpark, Tensorflow, Data Visualization: Tableau, ggplot, R Shiny, Plotly, Matplotlib, d3.js Big data Technologies: Hadoop, Spark, Kafka Cloud Technologies: Azure, AWS, Elastic Search Containers: Docker