SUMMARY:

• Over 7+ years of experience in Analytics, Visualization, Data modelling, Reporting.
• I have always been a technology aficionado, marveling at how data impacted our world, making it smaller, more efficient and well connected.
• Data Scientist with proven expertise in Data Analysis, Machine Learning, Big Data, and Modeling.
• Expertise in analyzing large volumes of data using various python packages and big data tools.
• Proficient in designing and developing various Machine Learning models with greater accuracy for prediction, clustering and recommendation tasks.
• Proven expertise in employing techniques for Supervised and Unsupervised (Clustering, Classification, PCA, Decision trees, KNN, SVM) learning, Predictive Analytics, Optimization methods and Natural Language Processing(NLP), Time Series Analysis.
• Experienced in advanced statistical analysis and predictive modeling in structured and unstructured data environment.
• Experienced in Machine Learning Classification Algorithms like Logistic Regression, K - NN, SVM, Kernel SVM, Naive Bayes, Decision Tree & Random Forest classification.
• Proficient in performing data wrangling operations to clean data from different sources and experimenting with predictive models and explanatory analyses to discover meaningful patterns.
• Good Understanding of working on Artificial Neural Networks and Deep Learning models using Theano and Tensorflow packages using in Python.
• Experienced in designing models using Neural Networks and Decision Trees.
• Worked on Relational (MySQL, DB2, Oracle) and NoSQL (HBase and MongoDB) databases.
• Experience working on BI visualization tools (Tableau, Shiny & QlikView.
• Experienced in using various components of Hadoop eco-system for data analysis and machine learning.
• Authored SQL Queries, Stored procedures, functions, created tables, views, triggers for relational databases

TECHNICAL SKILLS:

Programming Languages: Python, R, C++, Scala

Big Data: HDFS, Hive, HBase, Pig, Spark, Kafka

Web Technologies: HTML5, CSS, Javascript

Version Control: Git, Github

Database: MySQL, MongoDB, DB2, HBase, SQL, Spark SQL, HQL, Teradata, Postgres

Development Environments: Anaconda, Pycharm, Jupyter Notebooks, R Studio

Python skills: numpy, scipy, sklearn, pandas, matplotlib, nltk, beautifulsoup, pyunit, astropy, Confidential, fermipy.

Machine Learning: Linear Regresson, NLP, kNN, Clustering Analysis, Recommendation systems, Sentiment AnalysisRandom forests, Decision Trees, TensorFlow

Dimensionality Reduction: PCA, NMF, LDA

Visualization: Tableau, Qlikview

PROFESSIONAL EXPERIENCE:

Confidential, Maryland

Data Science Analyst

Responsibilities:

• Involved in Confidential project to develop a model to classify and predict black holes captured by K2 Fermi satellites
• Utilized Confidential (Automated Stellar Cluster Analysis) to perform cluster analysis.
• Designed a neural networks model with 4 million neurons to classify the images of Active Galactic Nuclei
• Utilized Apache Spark to collect real time sensor data from satellites and perform analysis using Hive.
• Proficient in using TensorFlow and Kears for designing and training CNN and RNNs.
• Hands on experience in implementing Naive Bayes and skilled in Random Forests, Decision Trees, Linear and Logistic Regression, SVM, Clustering, Principle Component Analysis.
• Partnered with various resources across the business to leverage their support and integrate our effort.
• Familiar with DAS (Data Acquisition System) and Confidential (Supervisory Control and Data Acquisation) systems
• Proficient in Data Acquisition, Storage, Analysis, Integration, Predictive Modeling and Cluster Analysis.
• Performed data cleaning, feature scaling, and feature engineering using pandas and numpy packages.
• Designed python scripts to store, retrieve and modify data in MongoDB and MySQL databases.
• Proficient in data pre-processing and interpreting patterns in datasets using pandas dataframes
• Performed data wrangling operations to clean data from different sources for predictive modelling
• Experienced in designing recommendation systems using AWS, Spark.
• Designed density estimators (kernel and nearest neighbors) using astroML package.
• Authored complex SQL queries for data retrieval and validate the accuracy of the data
• Performed data analysis using python pandas, R (for Multi-Dimensional Scaling), Hive

Confidential, New Jersey

Machine Learning Engineer

Responsibilities:

• Developing new methods of data extraction and data mining such as utilizing Natural Language Processing tools.
• Developed an NLP model using Confidential library in Python to classify the content in the investment portfolio database
• Expertise in using Linear Regression and Classification Modeling, Decision-trees, Principal Component Analysis
• Implemented Logistic Regression Algorithm resulting to identify the most valuable customers.
• Implement data mining and statistical machine learning solutions to various business problems.
• Used Python to analyze and identify the Security data lakes for the fraudulent transactions.
• Used NLP to text mine the customer service data lake for analyze the intent.
• Involved in defining the business/transformation rules applied for sales and service data.
• Involved in defining the source to target data mappings, business rules, data definitions.
• Responsible for defining the functional requirement documents for each source to target interface.
• Document, clarify, and communicate requests for change requests with the requestor and coordinate with the development and testing team.
• Coordinate 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.
• Developed and execute processes for accurate data capture across all clients to obtain key insights and relationships to overall business objectives using Statistical Hypotheses Model
• Document data quality and traceability documents for each source interface.
• Generate weekly and monthly asset inventory reports.

Confidential

Data Scientist

Responsibilities:

• Performed data extraction and analysis to develop business process mining model using BupaR
• Built forecast models using that improved planning and productivity by 25%.
• Conducted independent statistical analysis, descriptive analysis, hypothesis testing and logistic regression using R and SAS
• Created Dashboard reports using Tableau once the data analytics is completed and submit to the Business group
• Developed sophisticated data models to support automated reporting and analytics
• Developed personalized products recommendation with Machine Learning algorithms including collaborative filtering and Gradient Boosting Tree to meet the needs of existing customers and acquire new customers.
• Addressed overfitting by implementing of the algorithm regularization methods like L2 and L1.
• Implemented statistical modeling with XGBoost machine learning software package using Python to determine the predicted probabilities of each model.
• Used numpy, scipy, pandas, nltk(Natural Language Processing Toolkit), matplotlib to build the model.
• Used Principal Component Analysis in feature engineering to analyze high dimensional data.
• Performed data cleaning, features scaling, features engineering using pandas and numpy packages in python and build models using deep learning frameworks.
• Created deep learning models using Tensorflow and keras by combining all tests as a single normalized score and predict residency attainment of students.
• Implemented application of various machine learning algorithms and statistical modeling like Decision Tree, Text Analytics, Sentiment Analysis, Naive Bayes, Logistic Regression and Linear Regression using Python to determine the accuracy rate of each model.
• Designed reports that will use gathered metrics to infer and draw logical conclusions of past and future behavior.
• 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.

Confidential

Data Analyst

Responsibilities:

• Application of various machine learning algorithms and statistical modeling like decision trees, regression models, clustering, SVM to identify Volume using scikit-learn package in R.
• Worked on different data formats such as JSON, XML and performed machine learning algorithms in Python.
• Involved in transforming data from legacy tables to HDFS, and HBASE tables using Sqoop.
• Research on Reinforcement Learning and control (Tensorflow, Torch), and machine learning model.
• Partner with infrastructure and platform teams to configure, tune tools, automate tasks and guide the evolution of internal big data ecosystem; serve as a bridge between data scientists and infrastructure/platform teams.
• Worked on Text Analytics and Naive Bayes creating word clouds and retrieving data from networking platforms.
• Performed Exploratory Data Analysis, Data Wrangling and development of algorithms in R and Python for data mining and analysis.
• Extensively used Python's multiple data science packages like Pandas, NumPy, matplotlib, SciPy, Scikit-learn, Tensorflow and Confidential .
• Performed data analysis by gathering, spatial data from its pristine form to derive financial projections
• Rated stocks based on Fundamental Analysis, quarterly performance and future outlook
• Prepared Equity Research and Quarterly Earnings reports for companies under assigned sectors
• Involved in analysis, design and documenting business requirement specifications so as to build data warehousing extraction programs, end-user reports and queries.
• Used Google Fusion Tables and Tableau to publish visualizations.