ABOUT ME:

• 6 Years in Data Science
• 6 years in Information Technology
• Expertise in Machine Learning, Deep Learning, Convoluted Neural nets
• Projects involving NLP, NLG, Text Mining, Predictive AnalyticsArtificial Intelligence
• Techniques big data structure and unstructured

SUMMARY:

• Extensive exposure on analytics project life cycle CRISP - DM (Cross Industry Standard Process for Data Mining) and web applications using SCRUM methodologies.
• Use machine learning to advance systems such as product recommendations, search ranking and relevance, image attribution, demand routing, fit recommendations, inventory forecasting, threat modeling, etc.
• Business understanding, Data understanding, Data preparation, Modeling, Evaluation and Deployment.
• Experienced in practical application of data science to business problems to produce actionable results.
• Experience in Natural Language Processing (NLP), Machine Learning & Artificial Intelligence.
• Experience with AWS cloud computing, Spark (especially AWS EMR), Kibana, Node.js, Tableau, Looker.
• Able to in corporate visual analytics dashboards.
• Experience with a variety of NLP methods for information extraction, topic modeling, parsing, and relationship extraction
• Knowledge on Apache Spark and developing data processing and analysis algorithms using Python.
• Programming in Java, Python and SQL queries.
• Use of libraries and frameworks in Machine Learning such as NumPy, SciPy, Pandas, Theano, Caffe, SciKit-learn Matplotlib, Seaborn, Theano, TensorFlow, Keras, NLTK, PyTorch, Gensim, Urllib, Beautiful Soup).
• Experience working in industrial or manufacturing environments around Operations Analytics, Supply Chain Analytics, and Pricing Analytics.
• Ability with algorithms, data query and process automation.
• Evaluation of datasets and complex data modelling.

TECHNICAL SKILLS:

COMMUNICATION SKILLS: verbal, written, presentations

LEADERSHIP: supports project goals, business use case and mentors team

QUALITY: continuous improvement in project processes, workflows, automation and ongoing learning and achievement

CLOUD: Analytics in cloud-based platforms (AWS, MS Azure, Google Cloud)

ANALYTICS: Data Analysis, Data Mining, Statistical Analysis, Multivariate AnalysisStochastic Optimization, Linear Regression, ANOVA, Hypothesis Testing, ForecastingARIMA, Sentiment Analysis, Predictive Analysis, Pattern Recognition, Classification, Behavioral Modeling

PROGRAMMING LANGUAGES: Python, R, SQL, Scala, Java, MATLAB, C, SAS, F#

LIBRARIES: NumPy, SciPy, Pandas, Theano, Caffe, SciKit-learn Matplotlib, SeabornTensorFlow, Keras, NLTK, PyTorch, Gensim, Urllib, BeautifulSoup4, MxNet, Deeplearning4j, EJML, dplyr, ggplot2, reshape2, tidyr, purrr, readr

DEVELOPMENT: Git, GitHub, Bitbucket, SVN, Mercurial, PyCharm, Sublime, JIRA, TFSTrello, Linux, Unix

DATA EXTRATION AND MANIPULATION: Hadoop HDFS, Hortonworks Hadoop, MapRCloudera Hadoop, Cloudera Impala, Google Cloud Platform, MS Azure Cloud, both SQL and noSQL, Data Warehouse, Data Lake, SWL, HiveQL, AWS (RedShift, Kinesis, EMR, EC2)

MACHINE LEARNING: Supervised Machine Learning Algorithms (Linear RegressionLogistic Regression, Support Vector Machines, Decision Trees and Random Forests, Naïve Bayes Classifiers, K Nearest Neighbors), Unsupervised Machine Learning Algorithms (K Means Clustering, Gaussian Mixtures, Hidden Markov Models, D), Imbalanced Learning (SMOTE, AdaSyn, NearMiss), Deep Learning Artificial Neural Networks, Machine Perception

APPLICATIONS: Recommender Systems, Predictive Maintenance, Forecasting, Fraud Prevention and Detection, Targeting Systems, Ranking Systems, Deep Learning, Strategic Planning, Digital Intelligence

WORK EXPERIENCE:

Confidential, Florham Park, NJ

Data Scientist/Information Security Analyst

Responsibilities:

• Worked with the Identity Management Team within the Information Security Division to develop self-service tools for internal employees.
• Worked to establish cloud controls for identity governance and assess risks associated with cloud service providers.
• Utilized Security Information and Event Management log data to analyze potential unauthorized server communication.
• Data Science & Big Data · Simulation & Modeling. ·

Technologies Used: Python, Kubernetes, Splunk, Splunk Phantom, Microsoft Azure, NumPy, SKLearn, Django.

Confidential, Helena, MT

Data Scientist

Responsibilities:

• Worked in a Cloudera Hadoop environment using Python, SQL, and Tableau
• HDFS (Cloudera): Pulled data from Hadoop cluster.
• Worked within the Enterprise Applications team as a Data Scientist.
• Used Python, Pandas, NumPy, and SciPy for exploratory data analysis, data wrangling and, feature engineering.
• Used Tableau and TabPy for visualization of analyses.
• Worked along with Business Analyst, Data Analyst, and Data Engineers.
• Consulted with various departments within the company including, SIU and Safety.
• Managed and matched claim numbers into fraud cases.
• Cleaned fraud data to be joined with the claims data (~73k observations)
• Research and Assess the Fraud Predictive Analytics scenario in terms of predicting final outcomes for new claims
• Create a Tableau Dashboard that will help SIU in present their Annual Report
• Tried kernel density estimation in lower dimensional space as a feature to predict fraud.
• Testing Anomaly Detection Models such as Expectation Maximization, Elliptical Envelope, and Isolation Forest.
• Multivariate analysis of safety programs from the last 10 years.
• Used regression to determine the correlation of participation in the safety program with outcome of claims.
• Hypothesis testing and statistical analysis was done to determine statistically significant changes in claims after participating in the safety program.
• Presented findings of impact testing.
• Workers Compensation fraud detection
• Prepared data for exploratory analysis
• Engineering actuarial formulas
• Collaborated with other Data Scientist with use cases that included workplace accident prediction and sentiment analysis.

Technologies: Cloudera Hadoop, Python, SQL, and Tableau, Hadoop HDFS, Pandas, NumPy, and SciPy, TabPy, Data Modeling, Multivariate analysis, Regression Analysis, Hypothesis Testing, Exploratory Analysis, Sentiment Analysis, Predictive Analytics.

Confidential, McLean, VA

Data Scientist/NLP Engineer

Responsibilities:

• Oversaw the entire production cycle to extract and display metadata from various assets developing a report display that is easy to grasp and gain insights.
• Collaborated with both the Research and Engineering teams to productionize the application.
• Assisted various teams in bringing prototyped assets into production.
• Expertise in applying data mining techniques and optimization techniques in B2B and B2C industries and proficient in Machine Learning, Data/Text Mining, Statistical Analysis and Predictive Modeling.
• Utilized MapReduce/PySpark Python modules for machine learning & predictive analytics on AWS.
• Implemented assets and scripts for various projects using R,Java and Python
• Built sustainable rapport with senior leaders.
• Developing and maintaining Data Dictionary to create metadata reports for technical and business purposes.
• Build and maintain dashboard and reporting based on the statistical models to identify and track key metrics and risk indicators.
• Keeping up to date with latest NLP methodologies by reading 10 to 15 articles and whitepapers per week.
• Extracting the source data from Oracle tables, MS SQL Server, sequential files and Excel sheets.
• Parse and manipulate raw, complex data streams to prepare for loading into an analytical tool.
• Involved in defining the source to target data mappings, business rules, and data definitions.
• Project environment was AWS and Linux.

Technologies Used: Python, R, Java, Kubernetes, Docker, ELK Stack (ElasticSearch, Logstash, Kibana), AWS Comprehend

Confidential, Houston, TX

Data Scientist

Responsibilities:

• Application of data mining techniques and optimization techniques in B2B and B2C industries and Machine Learning, Data/Text Mining, Statistical Analysis and Predictive Modeling.
• Utilized PySpark Python modules for machine learning & predictive analytics in Hadoop on AWS.
• Predictive modeling using state-of-the-art methods.
• Implemented advanced machine learning algorithms utilizing caffe, TensorFlow, Scala, Spark, MLLib, R and other tools and languages needed.
• Programming, and scripting in R, Java and Python.
• Developed Data Dictionary to create metadata reports for tec hnical and business purpose.
• Built reporting dashboard on the statistical models to identify and track key metrics and risk indicators.
• Performed Boosting method on predicted model for the improve efficiency of the model.
• Extracted source data from Amazon Redshift on AWS cloud platform.
• Parsed and manipulated raw, complex data streams to prepare for loading into an analytical tool.
• Explored different regression and ensemble models in machine learning to perform forecasting
• Developed new financial models and forecasts.
• Improved efficiency and accuracy by evaluating models in R.
• Involved in defining the source to target data mappings, business rules, and data definitions.
• Performing an end to end Informatica ETL Testing for these custom tables by writing complex SQL Queries on the source database and comparing the results against the target database.

Confidential, Medina, MN

Data Scientist

Responsibilities:

• Applied Machine Learning, Data/Text Mining, Statistical Analysis and Predictive Modeling.
• Implemented Event Task for executing an application automatically.
• Involved in defining the source to target data mappings, business rules, and data definitions.
• Assist in continual monitoring, analysis and improvement of AWS Hadoop Data Lake environment
• Built and maintained dashboard and reporting based on the statistical models to identify and track key metrics and performance indicators.
• Involved in fixing bugs and minor enhancements for the front-end modules.
• Performed data mining and developed statistical models using Python to provide tactical recommendations to the business executives.
• Integrated R into micro-strategy to expose metrics determined by more sophisticated and detailed models than natively available in the tool.
• Participated in feature engineering such as feature intersection generating, feature normalize and label encoding with SciKit-Learn preprocessing.
• Worked on outlier identification with Gaussian Mixture Models using Pandas, NumPy and matplotlib.
• Adopted feature engineering techniques with 200+ predictors in order to find the most important features for the models. Tested the models with classification methods, such as Random Forest, Logistics Regression and Gradient Boosting Machine, and performed hyperparameter tuning to optimize the models.

Confidential, Chandler, AZ

Data Scientist

Responsibilities:

• Worked with internal architects and, assisting in the development of current and target state data architectures.
• Performed data quality in Talend Open Studio.
• Involved in defining the source to target data mappings, business rules, data definitions.
• Worked with stakeholders and analysts to identify system’s needs, requirements and helped Big Data Engineers translate those requirements into specifications.
• Created reporting visualizations to automate weekly and monthly reports.
• Responsible for defining the key identifiers for each mapping/interface.
• Responsible for defining the functional requirement documents for each source to target interface.
• Defined the list codes and code conversions between the source systems and the data mart.
• Implementation of Metadata Repository, Maintaining Data Quality, Data Cleanup procedures, Transformations, Data Standards, Data Governance program, Scripts, Stored Procedures, triggers and execution of test plans.
• Enterprise Metadata Library with any changes or updates.
• Coordinated with the business users in providing appropriate, effective and efficient way to design the new reporting needs based on the user with the existing functionality.
• Worked with BTEQ to submit SQL statements, import and export data, and generate reports in Teradata.

Confidential, Milwaukee, WI

Data Scientist

Responsibilities:

• Implemented Agile Methodology for custom application development using R and Python.
• Performed K-Means Clustering, Multivariate analysis and Support Vector Machines in Python.
• Utilized Apache Spark with Python to develop and execute Big Data Analytics and Machine learning applications, executed machine Learning use cases under Spark ML and MLlib.
• Participated in all phases of data mining; data collection, data cleaning, developing models, validation, visualization and performed Gap analysis.
• Used Pandas, NumPy, seaborn, SciPy, matplotlib, Scikit-Learn, NLTK in Python for utilizing various machine learning algorithms.
• Worked on different data formats such as JSON, XML and performed machine learning algorithms in Python.
• Data Manipulation and Aggregation from different sources using Nexus, Toad, Business Objects, Power BI and Smart View.
• Extracted data from HDFS and prepared data for exploratory analysis using data munging with R.
• Worked with Data Architects and IT Architects to understand the movement of data and its storage.
• Rapid model creation in Python using Pandas, NumPy, SKLearn and plot.ly for data visualization.