Kedarnath K Chimmad

# ad3v7i@r.postjobfree.com ï linkedin.com/in/kedarnathchimmad/ § github.com/KedarnathKC 413-***-**** Education

University of Massachusetts Amherst

MS in Computer Science, GPA: 3.9/4.00 Expected Graduation: May, 2025

• Coursework: Machine Learning, System for Data Science, Responsible AI, Natural Language Processing, Computer Vision

Vellore Institute of Technology Vellore, India

B.Tech in Computer Science, GPA: 3.95/4.00 May, 2021

• Coursework: Data Structures & Algorithms, Operating Systems, DBMS, Java Programming, Web Mining, Artificial Intelligence, Image Processing, Social & Information Networks, Data Visualization, Applied Linear Algebra Technical Skills

Languages: Python, Java, ABAP CDS, MySQL, HTML, CSS, JavaScript, PHP Libraries/Tools: Pandas, Numpy, Scikit-Learn, JAX, Matplotlib, Power BI, PySpark, ArcGIS Cloud Platform: Amazon Web Service

Experience

Shell Bengaluru, India

Associate Data Engineer Jul 2022 - Jul 2023

• As an ABAP CDS Developer in Shell’s S4 Transformation Program, conceptualized and architected data model to address intricate business requirements, providing a strategic roadmap for implementation.

• Spearheaded the development of a complex data model in Asset Management, providing a data-driven solution for accurate financial forecasting in asset maintenance. Reduced calculation time from hours to under a minute, benefitting supervisors and workers.

• Led Peer Code and Design Reviews, offering valuable insights and assisting colleagues in resolving data model design challenges.

Power BI Developer Oct 2021 - Jun 2022

• Collaborated with stakeholders to create 5 Power BI dashboards that enabled real-time progress tracking for scrum teams, informed decision-making, and helped the finance team identify overspending and forecast expenses. These dashboards also provided a holistic view of demands for the PMO team.

• Successfully integrated data from diverse sources, including Azure Dev Ops, Sharepoint Files and Sites, and IBM Blueworks.

Projects

Learning to Query Social Media via Interpretable ML

• Developed a sophisticated model leveraging Generalised Optimal Sparse Decision Trees (GOSDT) to automatically generate boolean queries based on a labeled US Election 2020 Twitter polls dataset of 50,000 Twitter poll datapoints, each characterized by 10,000 attributes.

• Achieved outstanding precision and accuracy of 96% and 92%, respectively, on the test set, demonstrating the model’s efficacy in capturing complex patterns in the data. Land Detection Using Object-Based Classification from Satellite Image Using ML

• Detected and classified various land types such as forest, developed, plantation/cultivation, barren, and water bodies through satellite images taken from Landsat. using ArcGIS

• Achieved 88.8% of accuracy using Mean Shift Segmentation and Support Vector Machine with a Kappa score of .83. Higher producer accuracy was achieved for the classification of Developed, Forest, and Baren Multi-Lingual Voice Based Railway System

• Developed mainly to help people who are post-lingual deaf and travelers who don’t know the local language of the area to get a ticket and get proper answers to their queries.

• This process will reduce the general time in getting the tickets through the counter from 10 minutes to 2 to 4 minutes through this system.

• This was a web-based project developed using HTML,CSS,JavaScript,PHP & Python

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