PROFESSIONAL SUMMARY:

• Around 15 years of experience in Machine Learning, Data mining with large data sets of structured and unstructured data, Data Acquisition, Data Validation, Predictive modeling, Data Visualization and discovering meaningful business insights.
• Proficient in applying Statistical Modelling and Machine Learning techniques (Linear Regression, Logistic Regression, Decision Trees, Random Forest, SVM, K - Nearest Neighbors, Bayesian, XG Boost) in Forecasting/ Predictive Analytics, Segmentation methodologies, Regression based models, Factor analysis, PCA, Ensembles and good knowledge on Recommendation Systems .
• Expertise in Scrapy and beautiful soup libraries for designing web crawlers.
• Excellent Software Development Life Cycle (SDLC) with good working knowledge of testing methodologies, disciplines, tasks, resources and scheduling.
• Excellent knowledge in Data Analysis, Data Validation, Data Cleansing, Data Verification and identifying data mismatch.
• Experience working with Web languages such as Html and CSS .
• Experience working with Weka and Meka (Multi-label classification).
• Strong Data Analysis skills using business intelligence, SQL and MS Office Tools.
• Strong SQL Server programming skills, with experience in working with functions, packages and triggers.
• Experience in visualization tools like, Tableau for creating dashboards.
• Hands on experience on building Recommendation Engines and Natural Language Processing.
• Expertise designing the web crawlers for data gathering and application of LDA.
• Strong experience in using Excel and MS Access to dump the data and analyse based on business needs.
• Knowledge in Cloud services such as Amazon AWS.
• Using Agile methodology to develop a project when working on a team.
• Expert in python libraries such as NumPy, SciPy for mathematical calculations, Pandas for data preprocessing/wrangling, Mat plot, Seaborn for data visualization, Sklearn for machine learning, Theano, TensorFlow, Keras for Deep leaning and NLTK for NLP.
• Expert in using Model Pipelines to automate the tasks and put models into production quickly.
• Expertise in Dimensionality Reduction techniques like PCA, LDA, Singular Value Decomposition technique.
• Expertise in k-Fold Cross Validation and Grid Search for Model Selection.
• Have knowledge of relational databases like Oracle SQL and SQLite.
• Experience in writing SQL, Sub Queries.
• Good knowledge on the five stages of Design Thinking Methodology.
• Excellence in handling Big Data Ecosystems like Apache Hadoop, MapReduce, Spark, HDFS Architecture, Cassandra, HBase, Sqoop, Hive , Pig, MLlib, ELT.
• Well experienced in Core Java - asynchronous programming, multithreading, collections and a few design patterns.
• Good Knowledge on Version control systems such as Git, SVN, Github, bitbucket.
• Explicitly fashioned in writing SQL queries for various RDBMS such as SQL Server, MySQL, Microsoft SQL, Postgre SQL, Teradata and Oracle , NoSQL databases such as MongoDB, HBase and Cassandra to handle unstructured data.
• Practically engaged in Evaluating Models performance using A/B Testing, K-fold cross validation, R-Square, CAP Curve, Confusion Matrix, ROC plot, Gini Coefficient and Grid Search.
• Robust participation for functioning in fast-paced multi-tasking environment both independently and in the collaborative team. Adequate with challenging projects and work in ambiguity to solve complex problems. A self-motivated exuberant learner.

Technical Skills

• Scikit: learns, Keras, TensorFlow, Numpy, Pandas, NLTK, Gensim, Matplotib, ggplot2, Scrapy, BeautifulSoup, Seaborn, Bokeh, networkx, Stats models, Theano.
• Programming Languages: Python, R, SQL, Scala, Pig, C, MATLAB, Java, C++, C
• Querying languages: SQL, NO SQL, PostgreSQL, MySQL, Microsoft SQL.
• Machine Learning: Data Preprocessing, Weighted Least Square, PCR, PLS, Picewise, Spline, Quadratic Discriminant Regression, Logistic Regression, Naive Bayes, Decision Tree, Random Forest, KNN, Linear Regression, Lasso, Ridge, SVM, Regression Tree, K-means, Ridge and Lasso, Polynomial Regression, Azure, Perceptron, Back Propagation,PCALDA. UML, RDF, SPARQL
• Big Data : Spark, AWS, EMR, S3, Kinesis, Lambdas, State-machines, Step-functions, SageMaker, IAM, DynamoDB, Hive, Genie, SQS, SNS, EC2, Spark Sql, Kafka, Scoop, Yarn, HDP, Machine Learning, AI, Cassandra, Druid, Redshift, Hadoop, Azure
• Tableau, Python: Matplotlib, Seaborn, Zoho
• Databases: SQL Lite, Hive, Oracle, MySql, Postgres, DynamoDB, Cassandra, Druid, Redshift, HBase, MS SqlServer, Aurora, PI Server (OSISoft)
• Tools: AWS Athena, SageMaker, Jupyter, Splunk, Informatica, Oracle OBIEE, Matlab, Weka, SPSS, Visio, Tibco Spotfire, Sql Developer, Cloudwatch, Airflow, Genie
• IDE Tools: PyCharm, Spyder, Eclipse, Visual Studio and NetBeans, Amazon Sagemaker.
• Project Management: JIRA, Share Point
• SDLC Methodologies: Agile, Scrum, Waterfall
• Anaconda Enterprise, R: Studio, AWS Lambda, Azure Machine Learning Studio, Oozie 4.2.