SUMMARY

• Professional qualified Data Scientist/Data Analyst nearly 8 years of experience in Data Science and Analytics including Artificial Intelligence/Deep Learning/Machine Learning, Data Mining and Statistical Analysis
• Involved in teh entire data science project life cycle and actively involved in all teh phases including data extraction, data cleaning, statistical modelling and data visualization wif large data sets of structured and unstructured data, created ER diagrams and schema.
• Experienced wif machine learning algorithm such as logistic regression, random forest, XGboost, KNN, SVM, neural network, linear regression, lasso regression and k - means
• Implemented Bagging and Boosting to enhance teh model performance.
• Strong skills in statistical methodologies such as A/B test, experiment design, hypothesis test, ANOVA
• Extensively worked on Python (NumPy, Pandas, Matplotlib, NLTK and Scikit-learn)
• Experience in implementing data analysis wif various analytic tools, such as Anaconda 4.0 Jupiter Notebook, R (ggplot2, Caret, dplyr) and Excel …
• Solid ability to write and optimize diverse SQL queries, working knowledge of RDBMS like SQL Server 2008, NoSQL databases like MongoDB.
• Developed API libraries and coded business logic using C#, XML and designed web pages using .NET framework, C#, Python, Django, HTML, AJAX
• Strong experience in Big Data technologies like Spark, Sparksql, pySpark, Hadoop, HDFS, Hive
• Experience in visualization tools like, Tableau for creating dashboards
• Excellent understanding Agile and Scrum development methodology
• Used teh version control tools like Git and build tools like Apache Maven/Ant
• Passionate about gleaning insightful information from massive data assets and developing a culture of sound, data-driven decision making
• Ability to maintain a fun, casual, professional and productive team atmosphere
• Experienced teh full software life cycle in SDLC, Agile, Devops and Scrum methodologies including creating requirements, test plans.
• Skilled in Advanced Regression Modeling, Correlation, Multivariate Analysis, Model Building, Business Intelligence tools and application of Statistical Concepts.
• Proficient in Predictive Modeling, Data Mining Methods, Factor Analysis, ANOVA, Hypothetical testing, normal distribution and other advanced statistical and econometric techniques.
• Developed predictive models using Decision Tree, Random Forest, Naïve Bayes, Logistic Regression, Social Network Analysis, Cluster Analysis, and Neural Networks.
• Experienced in Machine Learning and Statistical Analysis wif Python Scikit-Learn.
• Experienced in Python to manipulate data for data loading and extraction and worked wif python libraries like Matplotlib, Numpy, Scipy and Pandas for data analysis.
• Worked wif complex applications such as R, SAS, Matlab and SPSS to develop neural network, cluster analysis.
• Strong C#, SQL programming skills, wif experience in working wif functions, packages and triggers.
• Expertise in transforming business requirements into analytical models, designing algorithms, building models, developing data mining and reporting solutions dat scales across massive volume of structured and unstructured data.
• Skilled in performing data parsing, data ingestion, data manipulation, data architecture, data modelling and data preparation wif methods including describe data contents, compute descriptive statistics of data, regex, split and combine, Remap, merge, subset, reindex, melt and reshape.
• Experienced in Visual Basic for Applications and VB programming languages C#, .NET framework to work wif developing applications.
• Worked wif NoSQL Database including Hbase, Cassandra and MongoDB.
• Experienced in Big Data wif Hadoop, HDFS, MapReduce, and Spark.
• Experienced in Data Integration Validation and Data Quality controls for ETL process and Data Warehousing using MS Visual Studio SSIS, SSAS, SSRS.
• Proficient in Tableau and R-Shiny data visualization tools to analyze and obtain insights into large datasets, create visually powerful and actionable interactive reports and dashboards.
• Automated recurring reports using SQL and Python and visualized them on BI platform like Tableau.
• Worked in development environment like Git and VM.
• Excellent communication skills. Successfully working in fast-paced multitasking environment both independently and in collaborative team, a self-motivated enthusiastic learner.

TECHNICAL SKILLS

Languages: Java, Python, R, C#, SQL

Packages: ggplot2, caret, dply, gmodels, Edward, RCurl, tm, C50, twitteR, NLP, Reshape2, rjson, plyr, pandas, numPy, TensorFlow, seaborn, sciPy, matplot lib, scikit-learn, Beautiful Soup, Rpy2.

Web Technologies: JDBC, HTML5, DHTML and XML, CSS3, Web Services, WSDL, Django, AWS

Data Modelling Tools: Erwin, Rational Rose, ER/Studio, MS Visio, SAP Power designer.

Big Data Technologies: Hadoop, Hive, HDFS, Presto, MapReduce, Pig, Kafka, oozie.

Databases: SQL, Hive, Impala, Pig, Spark SQL, HQL, VQL, Databases SQL-Server, My SQL, MS SQL, MS Access, HDFS, HBase, Teradata, Netezza, MongoDB, Cassandra.

Reporting Tools: MS Office (Word/Excel/Power Point/ Visio), Tableau, Spotfire, Crystal reports XI, Business Intelligence, SSRS, Business Objects, Cognos

ETL Tools: Informatica Power Centre, SSIS.

Version Control Tools: SVM, GitHub

Project Execution Methodologies: Ralph Kimball and Bill Inmon data warehousing methodology, Rational Unified Process (RUP), Rapid Application Development (RAD), Joint Application Development (JAD).

BI Tools: Tableau, Tableau server, Tableau Reader, SAP Business Objects, OBIEE, QlikView, SAP Business Intelligence, Amazon Redshift, or Azure Data Warehouse

Operating System: Windows, Linux, Unix, Macintosh HD, Red Hat, Android.

PROFESSIONAL EXPERIENCE

Confidential

Data Scientist

Responsibilities:

• Performed Data Profiling to learn about behavior wif various features of USMLE examinations of various student patterns.
• Evaluated models using Cross Validation, Log loss function,ROCcurves and usedAUCfor feature selection and elastic technologies like ElasticSearch, Kibana etc
• Implemented statistical modelling wifXGBoostmachine learning software package using Python to determine teh predicted probabilities of each model.
• Created master data for modelling by combining various tables and derived fields from client data and students LORs, essays and various performance metrics.
• Formulated a basis for variable selection andGrid Search, KFoldfor optimalhyperparameters
• Utilized Boosting algorithms to build a model for predictive analysis of student’s behaviour who took USMLE exam apply for residency.
• UsedNumPy, SciPy, pandas, nltk (Natural Language Processing Toolkit), matplotlibto build teh model.
• Formulated several graphs to show teh performance of teh students by demographics and their mean score in different USMLE exams.
• Application of variousArtificial Intelligence (AI)/machine learning algorithms and statistical modelling like decision trees, text analytics,natural language processing (NLP), supervised and unsupervised,regression models.
• UsedPrincipal Component Analysisin feature engineering to analyze high dimensional data.
• Performed Data Cleaning, features scaling, features engineering using pandas andNumPypackages in python and build models using deep learning frameworks.
• Created deep learning models usingTensorFlowandkerasby combining all tests as a single normalized score and predict residency attainment of students.
• Used XGB classifier if teh feature is a categorical variable and XGB regressor for continuous variables and combined it usingFeature UnionandFunction Transformermethods of Natural Language Processing.
• UsedOnevsRestClassifier to fit each classifier against all other classifiers and used it on multiclass classification problems.
• Implemented application of various machine learning algorithms and statistical modelling likeDecision Tree, Text Analytics, Sentiment Analysis, Naive Bayes, Logistic Regression and Linear Regressionusing Python to determine teh accuracy rate of each model.
• Generated various models by using different machine learning and deep learning frameworks and tuned teh best performance model using Signal Hub.
• Created data layers as signals to Signal Hub to predict new unseen data wif performance not less TEMPthan teh static model build using deep learning framework.

Environment: Python, Hive, AWS, Linux, Tableau Desktop, Microsoft Excel, NLP, Deep learning frameworks such as TensorFlow, Keras, Boosting algorithms etc.

Confidential, Louisville, KY

Data Scientist

Responsibilities:

• Performed Data Profiling to learn about behavior wif various features such as traffic pattern, location, Date and Time etc.
• Application of various Artificial Intelligence (AI)/machine learning algorithms and statistical modelling like decision trees, text analytics, natural language processing(NLP), supervised and unsupervised,regression models,social network analysis, neuralnetworks, deep learning,SVM, clustering to identify Volume using scikit-learn package inpython,Matlab.
• UtilizedSpark, Snowflake, Scala, Hadoop, HQL, VQL, oozie, pySpark, Data Lake, TensorFlow, HBase, Cassandra, Redshift, MongoDB, Kafka, Kinesis, Spark Streaming, Edward, CUDA, MLLib, AWS, Python, a broad variety of machine learning methods including classifications, regressions, dimensionally reduction etc. and utilized teh engine to increase user lifetime by 45% and triple user conversations for target categories.
• Created and connected SQL engine through C# to connect database, developed API libraries and business logic using C#, XML and Python
• Exploring DAG’s, their dependencies and logs usingAirFlowpipelines for automation
• Performed data cleaning and feature selection using MLlib package inPySparkand working wif deep learning frameworks such asCaffe,Neonetc
• DevelopedSpark/Scala,Python, Rfor regular expression (regex) project in theHadoop/Hiveenvironment wifLinux/Windowsfor big data resources. Used clustering techniqueK-Meansto identify outliers and to classify unlabelled data.
• Created user friendly interface for quick view of reports by using C#, JSP, XML and developed expandable menu dat show drilldown data on graph click
• Evaluated models using Cross Validation, Log loss function,ROCcurves and usedAUCfor feature selection and elastic technologies like ElasticSearch, Kibana etc
• Categorised comments into positive and negative clusters from different social networking sites usingSentiment AnalysisandText Analytics
• Tracking operations using sensors until certain criteria is met usingAirflowtechnology.
• Responsible for different Data mapping activities from Source systems toTeradatausing utilities like Pump, FEXP, MLOAD, BTEQ, FLOAD etc
• Analyze traffic patterns by calculating autocorrelation wif different time lags.
• Ensured dat teh model TEMPhas low False Positive Rate and Text classification and sentiment analysis for unstructured and semi-structured data.
• Used Principal Component Analysis in feature engineering to analyze high dimensional data.
• Created and designed reports dat will use gathered metrics to infer and draw logical conclusions of past and future behavior.
• Performed Multinomial LogisticRegression,Random Forest,DecisionTree,SVMto classify package is going to deliver on time for teh new route.
• Performed data analysis by usingHiveto retrieve teh data fromHadoop cluster,Sqlto retrieve data fromOracledatabase and usedETLfor data transformation.
• UsedMLlib,Spark's Machine learning library to build and evaluate different models.
• Implemented rule-based expertise system from teh results of exploratory analysis and information gathered from teh people from different departments.
• Performed Data Cleaning, features scaling, features engineering using pandas andNumPypackages in python and build models using SAP Predictive Analytics.
• DevelopedMapReducepipeline for feature extraction usingHiveandPig.
• Created DataQuality ScriptsusingSQLandHiveto validate successful data load and quality of teh data. Created various types of data visualizations using Python andTableau /Spotfire.
• Communicated teh results wif operations team for taking best decisions.
• Collected data needs and requirements by Interacting wif teh other departments.

Enviro nment: Python, CDH5, HDFS, C#, Hadoop, Hive, Impala, AWS, Linux, Spark, Tableau Desktop, SQL Server 2012, Microsoft Excel, Matlab, Spark SQL, Pyspark.

Confidential, San Antonio, Texas

Data Science / BI Analyst

Responsibilities:

• Provided Configuration Management and Build support for more TEMPthan 5 different applications, built and deployed to teh production and lower environments.
• Implemented public segmentation using unsupervised machine learning algorithms by implementing k-means algorithm usingPyspark.
• UsingAirflowto keep track of job statuses in repositories like MySQl and Postgre databases.
• Explored and Extracted data from sourceXMLinHDFS, used ETL for preparing data for exploratory analysis using datamunging.
• Responsible for different Data mapping activities from Source systems toTeradata, Text mining and building models using topic analysis, sentiment analysis for both semi-structured and unstructured data.
• Handled importing data from various data sources, performed transformations using Hive, Map Reduce, and loaded data intoHDFS
• UsedRand python for Exploratory Data Analysis,A/Btesting, HQL, VQL, Data Lake,AWS Redshift, oozie,pySpark,Anovatest andHypothesistest to compare and identify teh TEMPeffectiveness of Creative Campaigns.
• Computing A/B testing frameworks, clickstream and time spent databases usingAirflow
• Created clusters to Control and test groups and conducted group campaigns usingText Analytics.
• Created positive and negative clusters from merchant’s transaction usingSentiment Analysisto test teh autanticity of transactions and resolve any chargebacks.
• Analysed and calculated teh lifetime cost of everyone in teh welfare system using 20 years of historical data.
• Created and developed classes and web page elements using C# and AJAX. JSP was used for validating client-side responses and connected C# to database to retrieve SQL data
• DevelopedLINUX Shellscripts by usingNZSQL/NZLOADutilities to load data from flat files toNetezzadatabase.
• Developed triggers, stored procedures, functions and packages using cursors and ref cursor concepts associated wif teh project usingPl/SQL
• Created various types of data visualizations using R, C#, python and Tableau/Spotfire also connected Pipeline Pilot wif Spotfire to create more interactive business driven layouts.
• UsedPython, R, SQLto create Statistical algorithms involving MultivariateRegression,Linear Regression,Logistic Regression,PCA, Random Forest models, Decision trees, Support Vector Machinefor estimating teh risks of welfare dependency.
• Identified and targeted welfare high-risk groups wifMachine learning/deep learning algorithms.
• Conducted campaigns and run real-time trials to determine wat works fast and track teh impact of different initiatives.
• Developed Tableau visualizations and dashboards usingTableauDesktop.
• Used Graphical Entity-Relationship Diagramming to create new database design via easy to use, graphical interface.
• Created multiple customSQL queriesinTeradata SQL Workbenchto prepare teh right data sets forTableaudashboards
• Perform analyses such asregression analysis, logistic regression, discriminant analysis, clusteranalysis usingSASprogramming.

Environment: R, HDFS, C#, Hadoop, Pig, Hive, Linux, R-Studio, Tableau, SQL Server, Ms Excel, Pypark.

Confidential, Pleasanton CA

Data Analyst

Responsibilities:

• Developed NLP algorithm to drive sales intention chats to appropriate agents using Deep Learning based on tensorflow in python language. Teh algorithm halped clients route chats to appropriate sales agent, and this halped boosting online sales by 20%.
• Worked on data mining tasks and data wrangling tasks on chat transcripts to convert and fed data into algorithms using python.
• Developed and deployed visitor scoring algorithm to identify teh customer intention using Gradient boosted decision trees and evaluation using scikit learn library. Teh combination of NLP and Visitor-Scoring halped clients boost overall conversations to conversion rate by 9 times.
• Proficient wif pandas, keras, tensorflow, pytorch, numpy, scikitlearn, matplotlib, seaborn Developed NLP algorithm to drive sales intention chats to appropriate agents using Deep Learning based on tensorflow in python language. Teh algorithm halped clients route chats to appropriate sales agent, and this halped boosting online sales by 20%. Worked on data mining tasks and data wrangling tasks on chat transcripts to convert and fed data into algorithms using python.
• Developed and deployed visitor scoring algorithm to identify teh customer intention using Gradient boosted decision trees and evaluation using scikit learn library. Teh combination of NLP and Visitor-Scoring halped clients boost overall conversations to conversion rate by 9 times.
• Proficient wif pandas, keras, tensorflow, pytorch, numpy, scikitlearn, matplotlib, seaborn
• Worked on a project of navigating rover bot wif a client to recognize traffic signs using neural networks. This project uses traffic signs image data as inputs. Teh bot uses its camera, and teh image gets classified in to appropriate action. This is mini project of “Computer Vision” dat further can be applied part of autonomous car.
• Data-preprocessing tasks included converting textual information into tabular data wif teh halp of machine learning algorithm libraries like nltk for NLP projects, OpenCV for computer vision projects and worked wif other machine learning preprocessing techniques like dimensionality reduction, feature selection and other data cleansing using python libraries numpy, pandas, auto-encoders to name a few.
• Working on OCR project to convert handwritten notes into identifiable tasks using machine learning algorithms like open-source computer vision and tesseract libraries.
• Prepared custom Tableau Dashboards from internally tracked data sources.
• Researched and tested statistical models on public datasets.
• Integrated custom chat solutions using javascript API methods.
• Integrated Adobe analytics and Google analytics tracking for custom events.
• Managed team using code collaborator and JIRA for work validation and performance appraisals.
• Managed and built end to end strategy for data collection and data transformation.
• Co-ordinated wif engineering team to publish Tableau dashboards.
• Involved in troubleshooting production issues, developing, and testing resolutions.
• Managing a team of developers and testing team.

Environment: s: Net, C#, JavaScript, PHP, SQL, Node.JS, Jira, Product Management, Data Analysis, SAS, Statistics, RDS Design, SQL, NoSQL, MongoDB

Confidential, Sturtevant, WI

Software Developer

Responsibilities:

• Led end-to-end development of a B2B software product which brought 35% efficiency in sales & inventory handling and
• 98% user satisfaction rate wifin recreational vehicle industry
• Built data visualization dashboards for teh sales and marketing team using SQL and Tableau to monitor product KPIs, reducing reporting time by 60%
• Led user interviews, research wif end-users and gathered key stakeholder pain points to understand user requirements and created a Minimal Viable Product
• Managed product roadmaps for several products dat resulted in reliable oversight, reporting, documentation, and ensured n-time product release
• Led teh development of a B2C mobile app in collaboration wif IT department and third-party development team, which reduced user onboarding time by 30% in teh higher education industry
• Owned data analysis to understand user behaviors to translate business requirements into technology solutions resulting in teh development of new features designed to gain 18% product growth
• Translated broad questions into actionable research plans, explored data and presented teh results through TEMPeffective storytelling and got new product development approval from teh stakeholders
• Performed quantitative and qualitative research by designing A/B tests and surveys to identify a B2C business pportunities and its viability for a tourism company

Environment: s: Net, C#, JavaScript, PHP, SQL, Node.JS, Jira, Product Management, Data Analysis, SAS, Statistics, RDS Design, SQL, NoSQL, MongoDB

Confidential, SFO, CA

Data Analytics (internship)

Responsibilities:

• Modeled teh dataset using DAX code to create 4 new columns
• Created 24 graphical visualizations including 4 dashboards using Power BI
• Forecasted sales & purchases, identified KPIs, analysed current trends and reviewed inventory stocks using pivot tables

Environment: s: SQL Server Management, Power BI, DAX