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Work Experience
Software Engineer
Tata Consultancy Services Ltd• March 2016 - December 2017
● Involved in the entire data science project life cycle and actively involved in all the phases including data extraction, data cleaning, statistical modeling and data visualization with large data sets of structured and unstructured data ● Involved in converting SQL queries into Spark transformations using Spark RDDs, Scala and python ● Analyzed the data to find the top selling product in each category. ● Experience in developing Spark programs in Python to perform Data Transformations, creating Data Frames, and writing spark SQL queries, spark streaming, windowed streaming application in Python them deployed in Yarn ● Experienced writing spark streaming and spark batch jobs using spark MLlib for analytics. ● Implemented Agile SCRUM project methodologies through incremental and iterative development to ensure validity ● Worked in Google Cloud Platform environment for development and deployment of custom Hadoop applications ● Environment: Spark, YARN, Scala, Python, Py-spark, Hadoop,GCP, Oracle, Linux/Unix, Shell Scripting ● Created visualizations for prediction trends and saturation points of products. Seaborn and Matplotlib. ● Extracted data using SQL queries, cleaned, imputed missing values and made the datasets ready for analysis. ● Predicted sale on the basis of area using machine learning algorithms such as Logistic regression, random forest to help clients understand sales python, pandas, numpy and sci kit-learn. ● Created ETL program for supporting Data Extraction, transformations and loading using Informatica Power Center. ● Developed and maintained inbound and outbound processes between CRM Applications and Master Data Sources (Oracle/ Netezza DB) through ‘Informatica’ and ‘Informatica Cloud’.
Education
University of Georgia
Computer Science, MS• January 2018 - December 2019
Pathan,S. Tripathi,A. https://arxiv.org/abs/2004.05698fbclid=IwAR1j522SGCsLbUN1q626RzVGrmDIq4js6CQGvh2rcM_WnhW2_mj1Z-e7vDE . ● Created a deep clustering architecture alongside image segmentation for medical image analysis. ● Main idea is based on unsupervised learning to cluster images on severity of the disease in the subject’s sample, and this image is then segmented to highlight and outline regions of interest. ● Developed an autoencoder on the images for segmentation. Python, Keras and tensorflow. ● Encoder part from the autoencoder branches out to a clustering node and segmentation node. ● Performed Deep clustering using K-means clustering at the clustering branch and a lightweight model is used for segmentation. ● Demonstrated our results on ISIC 2018: Skin Lesion Analysis Towards Melanoma Detection and Cityscapes datasets for segmentation and clustering. ● Proposed architecture beats U-Net and DeepLab results on the two datasets, and has less than half the number of parameters.
JSS Academy of Technical Education, Noida
Information Technology, B.Tech• 2011 - 2015
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