POOJA SHETTY

CONTACT

Phone: 988*******

Email: adlwyc@r.postjobfree.com

SKILLS

• Python

• Machine Learning

• Deep Learning

• Image Processing

• Data Analytics

• ETL Tools

• SQL

PROFILE SUMMARY

Experienced Software Developer with a history of working in Data Warehousing domain. Engineering professional with a Master's degree focused in Big Data and Data analytics from Manipal School of Information Sciences, MAHE, Manipal. Former Data Science Intern

(Research) at Philips India with a history of working on a healthcare project dealing with real time medical image data using deep learning algorithms, machine learning algorithms, medical image processing techniques.

EDUCATION

• Bachelor of Engineering in Computer Science at MITE, Mangalore (Affiliated to VTU Belgaum)

Duration: 2012 – 2016

Aggregate: 73.96%

• Master of Engineering in Big Data and Data Analytics at Manipal School of Information Sciences, MAHE, Manipal. Duration: 2018 – 2020

CGPA: 8.75

WORK EXPERIENCE

Role: Software Engineer at Tech Mahindra, Bengaluru. Duration: August 2016 – February 2018

Responsibilities:

• Developing, integrating and deploying ETL routines using ETL tools and external programming/scripting languages as necessary.

• Designing ETL logics for Data blending and transformation using Hive and automate the ETL jobs using tidal scheduler. INTERNSHIP DETAILS

Role: Research Intern at Philips India, Bengaluru. Duration: June 2019 – June 2020

Project Description: Worked on a live healthcare project dealing with exploration of AI models based on image processing features and traditional ML approaches for liver lesion characterization. In this project we developed algorithms to automatically identify the slices that have liver lesions for doctor’s review and characterize the detection of hepatocellular Carcinoma (HCC) lesions to generate LI-RAD score. Using LI-Rad score we predict the certainty of malignancy. ACADEMIC PROJECTS

• Heart attack prediction using machine learning approach: This is mini project done as a part of 2nd semester ME (Big Data and Data Analytics) curriculum. The goal was to formulate a model for effective prediction of occurrence of coronary heart disease. Comparison of accuracies generated by various machine learning algorithms. Selecting the best performing algorithms to predict the efficient results.

• Finding Related Posts using clustering and machine learning approach: This is a mini project done as a part of 1st semester ME (Big Data and Data Analytics) curriculum. The goal is to classify the related posts using clustering and machine learning approach. The main idea is when a user wants some information on a particular topic, the system takes the input and display the related posts to the topic. This was achieved using K-means algorithm to classify the files into different categories of posts with the minimum distance is displayed and also model is evaluated.

• Implementation of K-means algorithm for analyzing Big data using Hadoop: This project was done as a part of B.E. final year curriculum. This project is based on Hadoop and we implemented K-means clustering algorithm using Hadoop Map Reduce based on Geo location of the customers to provide more specialized and localized services.