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Rigved Manoj

Hey, My name is Rigved Manoj

I am an UMass Amherst CS grad student with 3 years of experience in data engineering. Passionate about tech innovation and AI's transformative role.

About Me I'm a data-driven Master's student at UMass Amherst, passionate about using my skills in data engineering and data science to solve real-world problems. Collaborative and driven by continuous learning, I'm eager to contribute meaningfully to innovative projects.

My Skills

Python
Spark
Hadoop
Hive
Scala
SQL
Data Analytics
Machine Learning
Computer Vision
Natural Language Processing
Reinforcement Learning
HTML
CSS
JavaScript
React
Django
Git
Agile

Experience My Previous Job Roles:

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Senior Data Engineer

Implemented a scalable, fault-tolerant data ingestion pipeline utilizing HDFS, Spark, and Scala for distributed processing and data manipulation, handling 15 million daily transactions.

Identified and resolved a data discrepancy across data centers, preserving $240k in revenue through root cause analysis, data lineage exploration, and code review

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Data Engineer

Created a modular, high-performance ETL tool with Python and Hive to automate data extraction and transformation across applications, achieving a 43% reduction in code redundancy and a 10% decrease in resource utilization.

Spearheaded the implementation of a scalable, config-driven utility for automating data pipeline testing, resulting in a 32% reduction in manual testing workload.

Successfully remediated 3 years of data inconsistencies involving 10 billion records through data lineage analysis, business logic refactoring, incremental data backfilling, and continuous monitoring to avoid disruption to end users.

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Data Science Intern

Built a full-stack forecasting application with React, Django, and Facebook’s Prophet for predicting transactional data, achieving an 18% accuracy improvement in anomaly detection.

Publications Computer Vision Research Papers I have published:

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A novel multiscale and multipath convolutional neural network based age-related macular degeneration detection using OCT images

Developed a multiscale CNN with 6 convolutional layers to extract and integrate local and global features, employing random forest classification for high-accuracy outcomes exceeding 96% across diverse datasets.

Published by Elsevier in Computer Methods and Programs in Biomedicine Journal on 2021/9/1

Paper
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RPE layer detection and baseline estimation using statistical methods and randomization for classification of AMD from retinal OCT

Innovated a novel approach utilizing contrast enhancement and pixel grouping to extract RPE layer and baseline from retinal OCT images for AMD diagnosis, achieving a 96.66% accuracy on a dataset of 2130 images.

Published by Elsevier in Computer Methods and Programs in Biomedicine Journal on 2021/3/1

Paper

Projects A few of my Projects:

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Reverse Chess

Led a 5-person team in developing a Python and Tkinter-based chess GUI with reverse rules, incorporating the Min Max algorithm, resulting in an AI opponent achieving an 80% win rate.

Github
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InstaHire

Designed a React and Django-based full-stack web app, employing RESTful APIs for a single-click Job-Board platform.

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Prioritized Sweeping in BlackJack

Implemented the Prioritized Sweeping algorithm in Python for Blackjack, resulting in a 70% win rate across 100 gameplays.

Github

Contact If you want to connect with me, send me a message below!