SUMMARY

• Over 8+ years of experience in areas including Data Analyst, Statistical Analysis, Machine Learning, Deep Learningwith large data sets of structured and unstructured data intravel services, strong functional knowledge, business processes, and latest market trends and manufactory industries.
• Developed predictive models using Decision Tree, Random Forest, Naïve Bayes, Logistic Regression, Cluster Analysis.
• Involved in the entire data science project life cycle and actively involved in all the phases including data extraction, data cleaning, statistical modeling, and data visualization with large data sets of structured and unstructured data, created ER diagrams and schema.
• Expert in the entire Data Science process life cycle including Data Acquisition, Data Preparation, Data Manipulation, Feature Engineering, Machine Learning Algorithms, Validation, and Visualization.
• Strong knowledge in Statistical methodologies such as HypothesisTesting, Principal Component Analysis (PCA), Sampling Distributions and Time Series Analysis.
• Extensively worked on Data preparation, exploratory analysis, Feature engineering using supervised and unsupervised modeling.
• Experienced in Machine Learning, Data mining with large datasets of Structured and Unstructured Data, Data Acquisition, Data Validation, and Predictive Modeling.
• Performed preliminary data analysis using descriptive statistics and handled anomalies such as removing duplicates and imputing missing values using Talend tool.
• Proficient in Python and its libraries such as NumPy, Pandas, Scikit - Learn, Matplotlib and Seaborn.
• Expert in prepossessing data in Pandas using visualization, data cleaning and engineering methods such as looking for Correlations, Imputations, Scaling and Handling Categories
• Experience in building various machine learning models using algorithms such as Linear Regression, Gradient Descent, Support Vector Machines (SVM), Logistic Regression, KNN, Decision Tree, Ensembles such as Random Forrest, AdaBoost, Gradient Boosting Trees.
• Experienced the full software lifecycle in SDLC, Agile, and Scrum methodologies.
• Strong SQL programming skills, with experience in working with functions, packages, and triggers.
• Excellent understanding of machine learning techniques and algorithms, such as k-NN, Naive Bayes, SVM, Decision Forestsetc.
• Experienced in Data Integration Validation and Data Quality controls for ETL process and Data Warehousing using MS Visual Studio SSIS, SSAS, SSRS.
• Expert in developing Data Conversions/Migration from Legacy System of various sources (flat files, Oracle, Non-Oracle Database) to Oracle system Using SQL LOADER, External table and Calling Appropriate Interface tables and API's Informatica.
• Drive core insights from available data to suggest A/B tests that drive improvements in site execution and experience
• Good Knowledge and experience in deep learning algorithms such as Artificial Neural network (ANN), Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN), LSTM and RNN based speech recognition using Tensor Flow.
• Excellent knowledge and experience in OLTP/OLAPSystem Study with focus on Oracle Hyperion Suite of technology, developing Database Schemas like Star schema and Snowflake schema (Fact Tables, Dimension Tables) used in relational, dimensional and multidimensional modeling, physical and logical Data Modeling using Erwin tool.
• Used Cognitive Science in Machine Learning for Neuro feedback training which is essential for intentional control of brain rhythms.
• Good understanding of working on Artificial Neural Networks and Deep Learning models using Theano and Tensor Flow packages using in Python.
• Extensive working experience with Python including Scikit-learn, SciPy, Pandas, and NumPy developing machine learning models, manipulating and handling data.
• Experience in developing and analyzing data models, involved in writing simple and complex SQL queries to extract data from the database for data analysis and testing
• Strong knowledge in all phases of the SDLC (Software Development Life Cycle) from analysis, design, development, testing, implementation, and maintenance with timely delivery against deadlines
• Proficient knowledge of statistics, mathematics, machine learning, recommendation algorithms and analytics with an excellent understanding of business operations and analytics tools for effective analysis of data.
• Excellent communication skills. Successfully working in fast-paced multitasking environment both independently and in a collaborative team, a self-motivated enthusiastic learner.
• Work closely with Product Managers to build KPIs and generate actionable insights from Enterprise data.

TECHNICAL SKILLS

Data Modeling Tools: Erwin r 9.6/9.5, ER/Studio 9.7, Star-Schema Modeling, Snowflake-Schema Modeling, FACT and dimension tables, Pivot Tables.

Operating Systems: Microsoft Windows, Linux/UNIX.

Scripting languages: HTML, DHTML, XML, JavaScript, JSON, XML, XSL, XSLT, JSP, JSTL.

Languages: C, C++, JAVA, HTML, Shell Scripting, PERL, R, Matlab,Scala, SQL, PL/SQL, ASP, Visual Basic, XML, SAS, Python, SQL, T-SQL, SQL Server.

Databases: Oracle 11g/12c, MS Access, SQL Server 2012/2014, Sybase and DB2, Teradata14/15, Hive.

Big Data Tools: Hadoop, Hive, Spark, Pig, HBase, Sqoop, Flume.

BI Tools: Tableau, Tableau server 8.2, Tableau Reader 8.1, SAP Business Objects, Crystal Reports, Toad for Oracle, Oracle SQL Developer, MS Word

Applications: MS Excel, MS Power Point, Teradata, Designer 6i.

Methodologies: RAD, JAD, RUP, UML, System Development Life Cycle (SDLC), Waterfall Model.

Packages: Microsoft Office 2010, Microsoft Project 2010, SAP and Microsoft Visio, Share point Portal Server

PROFESSIONAL EXPERIENCE

Data Engineer/Data Scientist

Confidential - Rhode Island

Responsibilities:

• Perform data manipulation, data preparation, normalization, and predictive modelling. Improve efficiency and accuracy by evaluating model in Python and R.
• Used Python and R for programming for improvement of model. Upgrade the entire models for improvement of the product.
• Implemented Spring boot microservices to process the messages into the Kafka cluster setup.
• Writing complex SQL queries for validating the data against different kinds of reports generated by Cognos.
• Filtered the discovered boundaries by implementing a non-max suppression algorithm to achieve an optimal bounding box per identified object.
• Implemented to reprocess the failure messages in Kafka using offset id.
• Worked on Big Data Integration &Analytics based on Hadoop, SOLR, Spark, Kafka, Storm and web Methods.
• Evaluated models using Cross-Validation, Log loss function, ROC curves and used AUC for feature selection and elastic technologies like Elasticsearch, Kibana, etc
• Implemented statistical modeling with XGBoostmachine learning software package using Python to determine the predicted probabilities of each model.
• Knowledge of monitoring, logging and cost management tools that integrate with AWS
• Created master data for modeling by combining various tables and derived fields from client data and students LORs, essays and various performance metrics.
• Used NumPy, SciPy, pandas, NTLK(Natural Language Processing Toolkit),Matplotlib to build the model.
• Worked in Hadoop Environment using pig, Sqoop, Hive, HBase and detailed understanding of MapReduce programs
• Involved in integration of various relational and non-relational sources such as Oracle, XML and Flat Files.
• Designed and implemented scalable Cloud Data and Analytical architecture solutions for various public and private cloud platforms using Azure.
• Created Data Pipeline using Processor Groups and multiple processors using Apache Nifi for Flat File, RDBMS as part of a POC using AmazonEC2.
• Formulated several graphs to show the performance of the students by demographics and their mean score in different USMLE exams.
• Application of various Artificial Intelligence (AI)/machine learning algorithms and statistical modeling like decision trees,text analytics, natural language processing(NLP), supervised and unsupervised, regression models for Predictive Analysis of data.
• Performed Data Cleaning, features scaling, features engineering using pandas and NumPy packages in python and build models using deep learning frameworks.
• Extensively used Apache Flume to collect the logs and error messages across the cluster.
• Created deep learning models using TensorFlow and Keras by combining all tests as a single normalized score and predict residency attainment of students.
• Used XGB classifier if the feature is a categorical variable and XGB regressor for continuous variables and combined it using FeatureUnion and FunctionTransfomer methods of Natural Language Processing.
• Created data layers as signals to Signal Hub to predict new unseen data with performance not less than the static model build using deep learning framework.
• Worked with the Data Governance group in creating a custom data dictionary template to be used across the various business lines.
• Create statistical models using distributed and standalone models to build various diagnostics, predictive and prescriptive solution.
• Interface with other technology teams to load (ETL), extract and transform data from a wide variety of data sources
• Provides input and recommendations on technical issues to Business & Data Analysts, BI Engineers,and Data Scientists.
• Developed SQL procedures to synchronize the dynamic data generated from GTID systems with the Azure SQL Server.
• Comprehensive knowledge and hands-on experience with service offerings from Amazon Web Services
• Expertise in building Azure native enterprise applications and migrating applications from on-premises to Azure environments
• Responsible for maintenance and monitoring of production/test/dev systems running on MS Azure

Environment: Python 2.x,3.x, Hive, PySpark +Java,AWS, Linux, Tableau Desktop, Microsoft Excel, Apache spark,NLP, Deep learning frameworks such as TensorFlow, Keras, Kafka,spark,Boosting algorithms,DB2, R, Python, Visio, HP ALM, Agile.

Data Engineer

Confidential -Alpharetta, GA.

Responsibilities:

• Extensively involved in all phases of data acquisition, data collection, data cleaning, model development, model validation and visualization to deliver data science solutions.
• 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.
• Define and deploy monitoring, metrics, and logging systems on AWS
• Developed predictive models on large scale datasets to address various business problems through leveraging advanced statistical modeling, machine learning.
• Implemented public segmentation using unsupervised machinelearning algorithms by implementing K-means algorithm by using PySpark using data munging. Analyzed and solved business problems and found patterns and insights within structured and unstructured data.
• Implemented advanced computer vision techniques like distortion correction, thresholding techniques, and the sliding window method to identify the lane markings to highlight the entire lane.
• Tested the algorithm in a video to ensure that the lane boundaries are accurately identified.
• Utilized a diverse array of technologies and tools as needed, to deliver insights such as R, SAS, MATLAB, Tableau and more.
• Detected near-duplicated news by applying NLP methods and developing machine learning models like label spreading and clustering.
• Good experience in writing Spark applications using Python and Scala.
• Employed the output of the semantic segmentation to perform drivable space estimation in 3D, lane estimation and to filter errors in the output of the 2D object detectors.
• Prototyping and experimenting withMLalgorithms and integrating into a production system for different business needs.
• Implemented Porter Stemmer (Natural Language Tool Kit) and NLP bag of words model (Count Vectorizer) to prepare the data.
• Implemented number of customer clustering models and these clusters are plotted visually using Tableau legends for the higher management.
• Developed SQL procedures to synchronize the dynamic data generated from GTID systems with the Azure SQL Server.
• Implemented Kafka producer and consumer applications on Kafka cluster setup with help of Zookeeper.
• Process automation using Python/R scripts with Oracle database to generate and write the results in the production environment on a weekly basis.
• Have knowledge on partition of Kafka messages and setting up the replication factors in Kafka Cluster.
• Used Data Quality validation techniques to validate Critical Data elements (CDE) and identified various anomalies.
• Closely worked with Kafka Admin team to set up Kafka cluster setup on the QA and Production environments.
• Performing Data Validation / Data Reconciliation between the disparate source and target systems for various projects.
• Implement systems that are highly available, scalable, and self-healing on the AWS platform
• Worked on multiple PoC’s onApache NiFi
• Writing complex SQL queries for validating the data against different kinds of reports generated by Cognos.
• Provides input and recommendations on technical issues to Business & Data Analysts, BI Engineers, and Data Scientists.

Environment: SAS, R, MLIB, Python,kafka,AWS, Data Governance, MDM, MATLAB, Tableau,Azure,Spark,Scala, SQL Server.

Data Engineer

Confidential - Dallas, TX

Responsibilities:

• Analyzed data using SQL, R, Python, Apache Spark, PySpark and presented analytical reports to management and technical teams.
• Worked with different datasets which includes both structured and unstructured data and Participated in all phases of Data mining, Data cleaning, Data collection, variable selection, feature engineering, developing models, Validation and Visualization.
• Lead discussions with users to gather business processes requirements and data requirements to develop a variety of conceptual, logical and Physical Data models and ETL using Informatica and Talend.
• Expertise in Business intelligence and Data Visualization tools like Tableau.
• Handled importing data from various data sources, performed transformations using Hive, Map Reduce and loaded data into HDFS.
• Designed and implemented a recommendation system which leverage Statistical Analytics data and the machine learning models and utilized Collaborative filtering techniques to recommend policies for different customers.
• Created Data Quality Scripts using SQL and Hive (HQL) to validate successful data load and quality of the data.
• Participated in features engineering such as feature generating, PCA, Feature normalization and label encoding with Scikit-learn pre-processing. Data Imputation using variant methods in Scikit-learn package in Python.
• Utilized Informatica toolset (Informatica Data Explorer and Data Quality) to inspect legacy data for data profiling.

Environment: SQL Server, Hive, Hadoop Cluster, AWS,ETL, Kafka,Tableau, Teradata, Machine Learning (Logistic regression/ Random Forests/ Collaborative filtering), Git Hub, MS Office suite, Agile

Data Engineer

Confidential - Tampa, FL

Responsibilities:

• Participated in all phases of data acquisition, data cleaning, developing models, validation, and visualization to deliver data science solutions.
• Retrieving data from SQLServer database by writing SQL queries like stored procedure, temp table, view.
• Worked with the DBA group to create a Best-Fit Physical Data Model from the Logical Data Model using Forward engineering using Erwin.
• Connected Database with Jupyter notebook for Modeling and Tableau for visualization and reporting.
• Worked on fraud detection analysis on loan applications using the history of loan taking with supervised learning methods.
• Used Pandas, NumPy, Seaborn, Matplotlib, Scikit-learn in Python for developing various machine learning models and utilized algorithms such as Logistic Regression, Random Forest, Gradient BoostDecisionTree, and NeuralNetwork.
• Experienced in performing feature engineering such as PCA for high dimensional datasets, important feature selection by Tree-based models.
• Perform model tuning and selection by using cross-validation, parameters tuning to prevent overfitting.
• Ensemble methods were used to increase the accuracy of the training model with different Bagging and Boosting methods.

Environment: SQL Server 2008, Python 2.x (NumPy/Pandas/Scikit-Learn), GitHub, Scrum, JIRA

Data Analyst/Data Engineer

Confidential

Responsibilities:

• Drew statistical inferences using t-tests, ANOVA, chi-sq tests and performed Post-Hoc Analysis using Tukey's HSD and Bonferroni correction to assess difference across levels of raw material categories, test significance of proportional differences and assess whether sample size is large enough to detect the differences.
• Provided statistical insights into semi-deviation &skewness-to-kurtosis ratio to guide vendor decisions and inferences into optimum pricing for order quantities.
• Performed Data Analysis on target data after transfer to Data Warehouse.
• Developed interactive executive dashboards using PowerBI and Excel VBA to provide a reporting tool that facilitates organizational metrics and data
• Worked with Data Architects on Dimensional Model with both Star and Snowflake Schemas utilized
• Created ETL solution using Informatica tool to read Product & Order data from files on shared network into SQL Server database.
• Created Database designs through data-mapping using ER diagrams and normalization upto the 3rd normal form and extracted relevant data whenever required using joins in PostgreSQL and Microsoft SQL Server.
• Conducted data preparation and outlier detection using Python.
• Worked with Team manager to develop a lucrative system of classifying auditions and vendor's best fitting for the company in the long run.
• Presented executive dashboards and scorecards to visualize and present trends in the data using Excel and VBA-Macros.

Environment: Microsoft Office, Microsoft PowerBI, Oracle SQL, Informatica, SPSS.

Systems Analyst

Confidential

Responsibilities:

• Performed data wrangling to clean, transform and reshape the data utilizing Pandas library. Analyzed data using SQL, R, Java, Scala, Python, Apache Spark and presented analytical reports to management and technical teams.
• Worked with different datasets which includes both structured and unstructured data and Participated in all phases of Data mining, Data cleaning, Data collection, variable selection, feature engineering, developing models, Validation and Visualization.
• Developed predictive models on large scale datasets to address various business problems through leveraging advanced statistical modeling, machine learning and deep learning.
• Implemented public segmentation using unsupervised machine learning algorithms by implementing K-means algorithm by using PySpark using data munging.
• Experience in Machine learning using NLP text classification, churn prediction using Python.
• Worked on different Machine Learning models like Logistic Regression, Multi-layer perceptron classifier and K-means clustering.
• Lead discussions with users to gather business processes requirements and data requirements to develop a variety of conceptual, logical and Physical Data models.

Environment: Hadoop, HDFS, Python 3.x (Scikit -Learn/ Keras/ SciPy/ NumPy/ Pandas/ Matplotlib/ NLTK), R (ggplot2/ caret/ trees/ arules), Tableau (9.x/10.x).