SUMMARY

• Professional experience spanning 17+ years in data science, data analytics, data mining, machine learning, business analytics, business intelligence, competitive intelligence, predictive analytics, forecasting, data acquisition, data validation, predictive modeling,data visualization& project managementacross domains of biostatistics, public sector, services, retail, BFSI, econometric, academic and HR domain.
• Professional and academic work experience with handling large data sets of structured and unstructured data.
• Hands on experience of working with APIs and doing analytics on the same
• Hands on experience in Natural Language Processing and Sentiment Analysis
• Hands on experience in undertaking unsupervised, supervised and reinforced Machine learning, analytics and visualizations using various packages of R and core analytical Python libraries such as Numpy, Scipy, Pandas and Scikit - learn and data visualization in Matplotlib,
• Extensive experience in performing the statistics test (ANOVA, Hypothesis testing and A/B test), Multivariate analysis and EFA/ PCA/CFA, etc
• Proficient in Statistical Modeling, Data Mining and Machine Learning Algorithms in Data Science/Forecasting/Predictive Analytics such as Linear and Logistics Regression, LDA, Item and Discriminate Analysis, Apriori, Random Forest, K Means, Artificial Neural Network,Decision Tree,SVM, K-Nearest, Bayesian, Hidden Markov, etc.
• Experience on developing dashboards using Tableau, PowerBI
• Experience working with relational databases like MySQL, MS Access, NoSQL databases like MongoDB
• Exposure to AI and Deep learning platforms/methodologies like tensor flow, RNN
• Experience in statistical packages like SPSS, SAS, Crystal Ball
• Experience in dealing with structured and semi-structured data inHDFS and Hive
• Familiar with the concepts of Hadoop, MapReduce framework
• Experience in doing data visualization and report dashboard in Tableau, Qlick view, and familiar withPower BI
• Maintained version control of Python using Git, Github
• Worked in Linux as well as Windows
• Knowledge of the Software Development Life Cycle (SDLC), Agileand Scrum
• A good team player and self-motivated by passion in data science

TECHNICAL SKILLS

Languages/Tools/Big Data: R3.X ( all major packages), Python 2.x/3.x (Numpy, Pandas, Scipy,Scikit-learn, Matplotlib)Hadoop/HDFS/Hive/Pig AWS

Machine Learning Algorithms: Linear and Logistics Regression, LDA, Item and Discriminate Analysis, Apriori, Random Forest, K Means, Artificial Neural Network, Decision Tree, SVM, K-Nearest, Bayesian, Hidden Markov, etc.

Visualizations: Ggplot,GoogleViz, Tableau, 3 D visualization, Power BI, Qlik, developing dashboards using Tableau, Power BI, etc

Statistical Tools: SPSS, Crystal Ball, SAS

PROFESSIONAL EXPERIENCE

Confidential, Boulder, CO

Data Scientist

Responsibilities:

• Developed applications of Machine Learning, Statistical Analysis and Data Visualizations with challenging data Processing problems in sustainability and biomedical domain.
• Compiled data from various sources public and private databases to perform complex analysis and data manipulation for actionable results.
• Gathers, analyzes, documents and translates application requirements into data models and Supports standardization of documentation and the adoption of standards and practices related to data and applications.
• Developed and implemented predictive models using Natural Language Processing Techniques and machine learning algorithms such as linear regression, classification, multivariate regression, Naive Bayes, Random Forests, K-means clustering, KNN, PCA and regularization for data analysis.
• Designed and developed Natural Language Processing models for sentiment analysis.
• Worked on Natural Language Processing with NLTK module of python for application development for automated customer response.
• Used predictive modeling with tools in SAS, SPSS, R, Python.
• Applied concepts of probability, distribution and statistical inference on given dataset to unearth interesting findings through use of comparison, T-test, F-test, R-squared, P-value etc.
• Applied linear regression, multiple regression, ordinary least square method, mean-variance, theory of large numbers, logistic regression, dummy variable, residuals, Poisson distribution, Bayes, Naive Bayes, fitting function etc to data with help of Scikit, Scipy, Numpy and Pandas module of Python.
• Applied clustering algorithms i.e.Hierarchical, K-means with help of Scikit and Scipy.
• Developed visualizations and dashboards using ggplot, Tableau
• Worked on development of data warehouse, Data Lake and ETL systems using relational and non relational tools like SQL, No SQL.
• Built and analyzed datasets using R, SAS, Matlab and Python (in decreasing order of usage).
• Applied linear regression in Python and SAS to understand the relationship between different attributes of dataset and causal relationship between them
• Performs complex pattern recognition of financial time series data and forecast of returns through the ARMA and ARIMA models and exponential smoothening for multivariate time series data
• Pipelined (ingest/clean/munge/transform) data for feature extraction toward downstream classification.
• Used ClouderaHadoop YARN to perform analytics on data in Hive.
• Wrote Hive queries for data analysis to meet the business requirements.
• Expertise in Business Intelligence and data visualization using R and Tableau.
• Expert in Agile and Scrum Process.
• Validated the Macro-Economic data (e.g. BlackRock, Moody's etc.) and predictive analysis of world markets using key indicators in Python and machine learning concepts like regression, Boot strap Aggregation and Random Forest.
• Worked in large scale database environment like Hadoop and MapReduce, with working mechanism of Hadoop clusters, nodes and Hadoop Distributed File System (HDFS).
• Interfaced with large scale database system through an ETL server for data extraction and preparation.
• Identified patterns, data quality issues, and opportunities and leveraged insights by communicating opportunities with business partners.
• Delivered and communicated research results, recommendations, opportunities, and supporting technical designs to the managerial and executive teams, and implemented the techniques for priority projects.

Environment: Machine learning, AWS, MS Azure, Cassandra, Spark, HDFS, Hive, Pig, Linux, Python (Scikit-Learn/Scipy/Numpy/Pandas), R, SAS, SPSS, MySQL, Eclipse, PL/SQL, SQL connector, Tableau.

Confidential, Milwaukee, WI

Sr. Data Scientist SME

Responsibilities:

• Applied concepts of probability, distribution and statistical inference on given dataset to unearth interesting findings through use of comparison, T-test, F-test, R-squared, P-value etc.
• Applied linear regression, multiple regression, ordinary least square method, mean-variance, theory of large numbers, logistic regression, dummy variable, residuals, Poisson distribution, Bayes, Naive Bayes, fitting function etc to data with help of Scikit, Scipy, Numpy and Pandas module of Python.
• Applied clustering algorithms i.e.Hierarchical, K-means with help of Scikit and Scipy.
• Built and analyzed datasets using R, SAS, Matlab and Python (in decreasing order of usage).
• Applied linear regression in Python and SAS to understand the relationship between different attributes of dataset and causal relationship between them
• Performs complex pattern recognition of financial time series data and forecast of returns through the ARMA and ARIMA models and exponential smoothening for multivariate time series data
• Pipelined (ingest/clean/munge/transform) data for feature extraction toward downstream classification.
• NLP and sentiment analytics using social media API calls
• Developing data mining; data analytics data collection, data cleaning, developing models, validation, visualization and performed Gap analysis.
• Performed K-means clustering, Kalman filtering, Multivariate analysis and Support Vector Machines in Python and R.
• Developed Clustering algorithms and Support Vector Machines that improved Customer segmentation and Market Expansion.
• Worked on NOSQL databases such as MongoDB and Cassandra.
• Experienced in Agile methodologies and SCRUM process.

Environment: Hadoop, Map-Reduce, HDFS, SQL, Pig,R, Python.

Confidential

Team leader Data Analyst

Responsibilities:

• Multivariate business and resource forecasting using machine learning algorithms. .
• Applied linear regression, multiple regression, ordinary least square method, mean-variance, theory of large numbers, logistic regression, dummy variable, residuals, Poisson distribution, Bayes, Naive Bayes, fitting function etc to data with help of Scikit, Scipy, Numpy and Pandas module of Python.
• Applied clustering algorithms on market data to study the underlying data patterns. Methodologies used were PCA, Factor analysis,Hierarchial, K-means through Scikit/Scipy, R for projecting market .
• Built and analyzed datasets using R, and Python.
• Applied linear regression in Python and SAS to understand the relationship between different attributes of dataset and causal relationship between them
• Performs complex pattern recognition of financial time series data and forecast of returns through the ARMA and ARIMA models and exponential smoothening for multivariate time series data
• Developed ETL based systems for data acquisition and data consumption by stakeholders.
• Developing data mining; data analytics data collection, data cleaning, developing models, validation, visualization and performed Gap analysis.
• Performed K-means clustering, Kalman filtering, Multivariate analysis and Support Vector Machines in Python and R.
• Developed Clustering algorithms and Support Vector Machines that improved Customer segmentation and Market Expansion.

Environment: Hadoop, Map-Reduce, SQL, R, Python, In house ETL tools.

Confidential

Data Analyst

Responsibilities:

• Multivariate biostatical and environment data analytics.
• Applied linear regression, multiple regression, ordinary least square method, mean-variance, theory of large numbers, logistic regression, dummy variable, residuals, Poisson distribution, Bayes, Naive Bayes, fitting function etc to data with help of Scikit, Scipy, Numpy and Pandas module of Python.
• Applied decision tree and neural network based systems for forecasting and predictive analytics. Methodologies used were RNN, KnN, SMV, PCA, Factor analysis, Hierarchial, K-means through Scikit/Scipy, R, etc.
• Built and analyzed datasets using R, and Python.
• Applied linear regression in Python and SAS to understand the relationship between different attributes of dataset and causal relationship between them
• Pipelined (ingest/clean/merge/transform) data for feature extraction toward downstream classification.
• Developing data mining; data analytics data collection, data cleaning, developing models, validation, visualization and performed Gap analysis.
• Performed K-means clustering, Kalman filtering, Multivariate analysis and Support Vector Machines in Python and R.
• Developed Clustering algorithms and Support Vector Machines that improved Customer segmentation and Market Expansion.

Environment: MS SQL, SAS, R, Vector Machines, Python.

Confidential

Lead Consultant, Data base Management

Responsibilities:

• Developed the program management, program monitoring and evaluation framework of 27 mission mode project for data acquisition, data dissemination and data interoperability
• Statistical analysis and resource forecasting systems using statistical tools and packages like SPSS, SAS, etc..
• Applied linear regression, multiple regression, ordinary least square method, mean-variance, theory of large numbers, logistic regression, dummy variable, residuals, Poisson distribution, Bayes, Naive Bayes, fitting function etc to data with help of Scikit, Scipy, Numpy and Pandas module of Python.
• Applied clustering algorithms on market data to study the underlying data patterns. Methodologies used were PCA, Factor analysis, Hierarchial, K-means through Scikit/Scipy, R for projecting market .
• Built and analyzed datasets using R, and Python.
• Projecting casual - effect relationship among various components of the project using open tools.
• Performing complex pattern recognition of the time series data for analytical purposes,
• Developed ETL based systems for data acquisition and data consumption by stakeholders.
• Developing data mining; data analytics data collection, data cleaning, developing models, validation, visualization and performed Gap analysis.

Environment: Red Hat, MS, SAS, SPSS, SQL, Data Warehousing, R, Python, MS Access, In house ETL tools.

Confidential

Data Technology Analyst

Responsibilities:

• Developing BI/CI solution for senior stakeholders
• Statistical analysis and resource forecasting systems using statistical tools and packages like SPSS, SAS, etc..
• Developing forecasting and resource projection solutions using open source and proprietary tools.
• Projecting casual - effect relationship among various components of the project using open tools.
• Performing complex pattern recognition of the time series data for analytical purposes,
• Developed ETL based systems for data acquisition and data consumption by stakeholders.
• Developing data mining; data analytics data collection, data cleaning, developing models, validation, visualization and performed Gap analysis.

Environment: Red Hat, MS, SAS, SPSS, SQL, Data Warehousing & OLAP tools.