PROFESSIONAL SUMMARY:

Professional experience spanning 17+ years in data science, data analytics, education, data mining, machine learning, business analytics, business intelligence, competitive intelligence, predictive analytics, forecasting, data acquisition, data validation, predictive modeling ,data visualization & project management across domains of biostatistics, public sector, services, retail, BFSI, & academic domain.

TECHNICAL SKILLS:

Languages /Tools/Big Data: Matlab, R3.X ( all major packages), Python 2.x/3.x (Numpy, Pandas, Scipy, Scikit - learn, Matplotlib), HTML, CSS, Django, Kubernetes( Working Knolwdge), Docker( Working Knowledge), NodeJs, SQL, Android and IoS SDK, AWS etc.

Machine Learning Algorithm s: PCA, Factor Analysis, 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, AWS ML, 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 WORK EXPERIENCE:

Confidential, Edison, NJ

Advisor

Responsibilities:

• Fine tuning and revamping the current programs being offered by Confidential
• Institutional research including analysis of student performance review, testing and assessment at all levels, statistical modelling of program delivery and design, psychometric analysis of potential/current/ ex students which might have a bearing on the program delivery.
• Identifying and onboarding faculty for program delivery for current and future programs to be offered by Sollers.
• Managing and ensuring best practices for student engagements during the course delivery including but not limited to student’s services, students relations, grievance handling, mentorship and others
• Research on the new industry trends and including the same into instructional design and models.
• Undertaking cost-benefit analysis of the program and suggest senior management on steps to improve cost-benefit of the current and future programs.
• Developing and implementing process quality assurance systems relating to onboarding, program delivery and program exit of students
• Managing and developing best practices and systems for requisite financial assistance to be provided to the students that may have impact or have a bearing impact on the efficacy and efficiency on the program delivery.
• Ensuring compliance of the program delivery system based on the self-specified guidelines
• Faculty role in teaching data science courses
• Establishing data science and big data consultancy practice
• Established employer backed data science and big data practices educational offerings a complete end to end academic and consultancy programs for the organizations.
• Established stakeholders oriented marketing systems which includes establishing a startup accelerator program with GAN(affiliate of world’s largest startup accelerator program with global outreach in 120 companies) and membership oriented activities with Linux Foundation( world’s largest membership based organization for organizations working on Linux platform.
• Upgraded and established in demand skills and industry focused service offerings etc.
• Established and fine-tuned the Income Equity Model for student financing(Income Sharing Agreement)
• End to end program management best practices for launching new programs in domains of ML, AI, NLP, Computer vision, biostatistics, health care analytics Big Data Engineering including the compliance and program delivery system in line with ISO standards
• Established Investor Centric M&E Operations
• Onboarding employers and business partners to expand consultancy and incubation operations of the organizations in data science practice

Technology and Application Pipelines:

• Python pipeline for NLP application using Spacy, CoreNLP
• AI/ML Applications using Python and R
• Dashboards using Tableau and Power BI
• Forecasting and predictive modelling

Confidential, Potsdam, NY

Research Assistant

Responsibilities:

• Career growth and attrition analytics on public database of IBM Employees Database: The project analyzed growth and career advancement factors, attrition, HR dynamics and potential insights.
• Job advertisement analytics using machine learning methods in finding connect through KnN and other methods. The project undertook API based analytics to analyze posting based connect through geographical and content based classifiers.
• Interactive visualization was used for zoom in and zoom out function based on geographical classifiers .
• Platforms used: Python(Pandas, Numpy and other relevant libraries), R
• Visulisations: Tableau, R, PowerBI
• Analyzing Confidential Collusion data and analyzing findings for process and traffic management improvisation. The project involved using NY Open Data pull through API (csv format) and analyzing the data
• Platforms used: Python(Pandas, Numpy and other relevant libraries), R
• Visualization: Tableau, Power BI

Confidential, Boulder, CO

Visiting Research Scholar

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, and Python (in decreasing order of usage).
• Pipelined (ingest/clean/munge/transform) data for feature extraction toward downstream classification.
• 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.
• 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, Python (Scikit-Learn/Scipy/Numpy/Pandas), R, SPSS, MySQL, Eclipse, PL/SQL, SQL connector, Tableau.

Research Lead

Confidential

Responsibilities:

• Developed the research methodologies and systems of using power of technology to develop integrated and collaborated public datasets for analytics
• 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

Lead Consultant

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.