SUMMARY

• 7 years of experience in the Data Science domain, with knowledge in advanced Machine learning and Natural Language Processing (NLP) algorithms
• Extensive knowledge of Algorithms, Data Structures, multithreading, OOP, distributed applications, and client - server architecture
• Strong analytical skills gained from working with data from diverse domains and formats- both structured and unstructured
• Derived beneficial insights for business environments through Exploratory Data Analysis (EDA), feature engineering, model building, tuning, data validation and model implementation
• Implemented Big Data platforms in multi-cloud environment, including AWS SageMaker, and Azure
• Worked in the entire data science project life cycle and actively involved in all the phases including data extraction, data cleaning, modeling and data visualization with large data sets of structured and unstructured data
• Extensive experience in Text Analytics, developing different Statistical Machine Learning, Data Mining solutions to various business problems and generating data visualizations using R, Python
• Optimized data pipelines, data flow, and collection for cross-functional teams
• Experienced with Machine Learning Algorithms such as Logistic Regression, KNN, SVM, Random Forest, Neural Network, Linear Regression, Lasso Regression and K-Means
• Experienced working with data modeling tools like Erwin, Power Designer and ER Studio. Expertise in synthesizing Machine Learning, Predictive Analytics and Big data technologies into integrated solutions
• Creating from scratch Machine Learning and NLP solutions for Big Data on top of Spark using Scala. Experienced in developing Entity-Relationship diagrams and modeling Transactional Databases and Data warehouse using tools like ERWIN, ER/Studio and Power Designer and experienced with modeling using ERWIN in both forward and reverse engineering cases
• Skilled in Advanced Regression Modeling, Time Series Analysis, Statistical Testing, Correlation, Multivariate Analysis, Forecasting, Model Building, Business Intelligence tools and application of Statistical Concepts
• Proficient in data entry, data auditing, creating data reports & monitoring data for accuracy and ability to extract Web search and data collection, Web data mining, Extract database from website, Extract Data entry and Data processing
• Experience with Data Analytics, Data Reporting, Ad Hoc Reporting, Graphs, Scales, PivotTables and OLAP reporting
• Expert in data flow between primaryDB and various reporting tools, Expert in finding Trends and Patterns within Datasets and providing recommendations accordingly
• Ability to use dimensionality reduction techniques and regularization techniques

TECHNICAL SKILLS

Languages: Python, R, SAS, Java, SQL, Scala, Javascript

Libraries: Pandas, Numpy, scikit-learn, H2O, Tensorflow, Pytorch,Pyspark

Databases: PostgreSQL, SQL Server, MS-Access, Oracle and Teradata,Hadoop, Mahout, MLlib, H2O, Cloudera Oryx, Mongo, Snowflake

Web: HTML, CSS, PHP, XML/XAML, REST API

Machine Learning: Regression, Classification, Clustering, Association, Logistic Regression, Simple Linear Regression, Polynomial Regression, Decision Trees, Random Forest, Multiple linear Regression, K-Nearest Neighbors (K-NN), Kernel SVM

BI Tools: Tableau,Power BI, QlikView,Business Objects

Frameworks: Shogun, Accord, Scala, Spark

PROFESSIONAL EXPERIENCE

Confidential, TX

Data Scientist/Machine Learning

Responsibilities:

• Involved in text pre-processing and normalization techniques such as Sentence tokenization, lemmatization, stemming, part-of-speech tagging
• Performed hash tagging using extracted n-grams
• Used Machine Learning Key Phrase extraction algorithm to get structured information from unstructured data
• Used Sentiment analysis and Deep semantic similarity algorithms where semantic score is based on similarity of meaning
• Advanced visualization techniques like Power BI dashboards to communicate the sentiment tied to user experience response as positive, negative, or neutral
• Use of Azure Data Lake to store the data, SQL Server Analysis Services to create multidimensional cubes

Confidential

Data Scientist/Machine Learning

Responsibilities:

• Feature extraction using histogram of colors to define colors and histogram of oriented gradients to define shapes, SIFT, SURF and indexing images by their visual content
• Prepared Dataset with annotation files for each image to generate bounding boxes
• Faster RCNN was used for detecting the type of vascular lesions based on color, texture and size
• K Means clustering algorithm was used to cluster the type of the detected lesion
• After detection is done all the details will be included in the JSON file
• Extension of the project will be increasing to some more application areas -spine pathology
• Experienced in building models by using Spark (PySpark, Spark SQL, Spark MLLib, and Spark ML)

Confidential

Data Science/ Data Engineer

Responsibilities:

• Responsible for performing Machine-learning techniques regression/classification to analyze historical data and predict the outcomes.
• Responsible for design and development of advanced R/Python programs to prepare to transform and harmonize data sets in preparation for modeling.
• Developed large data sets from structured and unstructured data
• Developed and supported data pipelines to extract, transform and load data into central warehouse
• Partnered with modelers to develop data frame requirements for projects.
• Performed Ad-hoc reporting/customer profiling, segmentation using R/Python.
• Tracked various campaigns, generating customer profiling analysis and data manipulation.
• Provided R/SQLprogramming, with detailed direction, in the execution of data analysis that contributed to the final project deliverables. Responsible for data mining.
• Analyzed large datasets to answer business questions by generating reports and outcome- driven marketing strategies
• Optimized data pipelines, data flow, and collection for cross-functional teams
• Created and maintained optimal data pipeline architecture by assembling large, complex data sets to meet functional and non-functional business requirements
• Experience in the container, streaming and messaging technologies including Docker, kubernetes
• Extensive experience with analytics databases like Snowflake