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Analyst Resume

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Charlotte, NC

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TECHNICAL SKILLS:

Domains: Banking, Insurance, Healthcare, Securities

Programming Languages: Java, C++, R, Scala (UDF), spark, spark mllib, hive, python, pyspark

Big data API: Spark, SparkR, Hadoop, map reduce, oozie workflow

Data Warehouse Reporting & Integration Tools: SSIS, SSRS

Databases: HDFS, MySQL, SQL, DB2, Oracle

ML models worked on: Random Forest, Decision tree, KNN, word2Vec, Neural network, SVM, Logistic regression, time series regression model, Information Retrieval, Deep learning with nlp, numpy,pandas, scikit

Data visualization: gglpot, seaborn, matplotlib

Scripting: scala, python, R

PROFESSIONAL EXPERIENCE:

Analyst

Confidential, Charlotte, NC

Responsibilities:
  • Involved in creating machine learning models to find anomalies in data - numerical in spark ML
  • Automate classification of data transformation types using machine learning using - decision tree classifier. Created spark scala script with UDFs that extract required features form data and feed it to decision tree classifier to train the model and use it for automating data transformation classification in capturing data lineage for audits
  • Created linear regression models in spark ML that learns the trend in key amount fields of given file and use it to predict future values. Metrics MSE, RMSE, R squared deployed to evaluate the prediction model
  • Classifying customers as bad and good customers based on features like FICO score, balance - KNN, decision tree classifier, One vs all, Multilayer perceptron, Logistic regression, Random Forest in spark ML. Metrics - F1, precision, recall are used to evaluate the model accuracy.
  • Created time series regression prediction model to predict customer balance amount using holtwinter and ARIMA time series in R studio. Data used in research are from hdfs

Graduate Research

Confidential, Clemson, South Carolina

Responsibilities:
  • Involved in installation, deployment and performance evaluation of interface SparkR
  • Have successfully tested statistical computation in spark. Fine tuning parameters set up in SparkR

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