About
Highly analytical and results-oriented Data Scientist with expertise in deep learning, computer vision, and advanced analytics. Proven ability to develop and deploy production-grade machine learning solutions, optimize data pipelines, and drive impactful insights across diverse domains including financial technology, sports analytics, and road safety. Seeking to leverage strong technical skills and leadership experience to solve complex data challenges and contribute to innovative product development.
Work
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Summary
Collaborated with Data Engineering and Data Science teams to design scalable dashboards and automate data pipelines.
Highlights
Collaborated with Data Engineering and Data Science teams to design scalable Apache Superset dashboards and automate data pipelines using Python, SQL, and Airbyte, optimizing task scheduling with DAGs.
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Summary
Developed and fine-tuned deep learning models for real-time sports ball tracking.
Highlights
Developed and fine-tuned deep learning models (TrackNet) for real-time squash ball tracking using Python, TensorFlow, PyTorch, and OpenCV.
Generated comprehensive datasets and processed match footage with FFmpeg, yielding key insights into player behavior and match dynamics.
Deployed models in Docker environments and collaborated with cross-functional teams to integrate tracking data into broadcasting systems, significantly enhancing viewer engagement.
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Summary
Leading the end-to-end development of a flagship production tool for financial technology.
Highlights
Led end-to-end development of 'FinSpector', a flagship production tool for bank statement parsing, transaction categorization, and mule detection, positioning it among a select few platforms in India.
Engineered a robust PDF and OCR pipeline leveraging regex and NLP to achieve 97%+ accuracy in transaction extraction and classification, deploying a rule-based fraud engine to identify suspicious patterns.
Enabled clients to perform real-time creditworthiness assessment and fraud detection directly from uploaded statements, reducing manual underwriting time by 60%.
Positioned the tool for enterprise rollout with SaaS pricing, forecasting significant revenue opportunities through B2B lending partnerships.
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Summary
Conducted a comparative study on road safety datasets and implemented computer vision models for traffic violation detection.
Highlights
Conducted a comparative study of road safety datasets and blackspot definitions across India, UK, US, and France, identifying critical feature gaps and inconsistencies in Indian data.
Implemented YOLO-based computer vision models with centroid tracking to detect traffic violations including triple riding, helmet-less riding, and wrong-way driving.
Developed a web scraping tool using BeautifulSoup4 to extract and structure FIR data from Karnataka (post-2016) via bounding box-based parsing.
Performed spatio-temporal analysis of violations across 50 Bengaluru traffic junctions, integrating IUDX, BTP, and meteorological datasets to identify violation trends and build predictive models.
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Summary
Contributed to the development of a recommendation engine and improved real-time player tracking solutions.
Highlights
Developed a recommendation engine to automate storyline generation for cricket commentators.
Tested and benchmarked the company's real-time player tracker solution (QT and QStat), improving efficiency by 20%.
Contributed to the development of new YOLO-based object detection models for cricket ball tracking, leveraging highlight videos for real-time detection and tracking in QT (Quidich Tracker).
Education
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B.Tech
Computer Science and Engineering
Grade: CGPA: 7.5
Courses
Served as the Chairman of ACM LNMIIT Students Chapter.
Served as the Coordinator of the Gender Sensitization and Equality Council.
Elected as the Senator of the Student's Gymkhana, LNMIIT 2022-23 (Student's Council body).
Awards
Research Paper Acceptance: Exploring Anomaly Detection Techniques for Crime Detection
Awarded By
ICRTCIS 2024 / Springer Book Series
Paper accepted for presentation at ICRTCIS 2024 conference and published in the Springer Book Series 'Algorithms of Intelligent Systems'.
Research Paper: Cognizance of the Premier League (Peer-Review Process)
Awarded By
IJSMM by InderScience
Paper on football analytics currently undergoing peer-review for publication in a Q2 journal.
Chanakya UG Fellowship
Awarded By
iHUB DivyaSampark, IIT Roorkee
Awarded prestigious fellowship as the only shortlisted team from Jaipur.
TATA Crucible Campus Quiz 2022 Finalist (Rajasthan Zone)
Awarded By
TATA Crucible
Achieved a top 6 spot out of 20,000+ students in Rajasthan zone finals and 1st in Cluster wildcard round.
Publications
Cognizance of the Premier League: An In-Depth Exploration of Team Performance, Player Transfers, Referee Dynamics, and Player Position Prediction for Scouting
Published by
IJSMM by InderScience (peer-review process)
Summary
Exploration of the IPL auction market using statistical analysis and machine learning models to uncover insights into player valuation and predict auction prices.
Languages
English
Fluent
Hindi
Native
Skills
Programming Languages
Python, C++, C.
Libraries & Frameworks
PyTorch, TensorFlow, OpenCV, Scikit-learn, Matplotlib, Seaborn, Spacy, Apache Superset, Airbyte.
Tools & Platforms
Docker, Amazon AWS, GCP, Tableau, CleverTap, FFmpeg, BeautifulSoup4, HTML5, CSS, Javascript, PHP, Unreal Engine.
Databases
MySQL.
Machine Learning
Deep Learning, Computer Vision, Predictive Modeling, Anomaly Detection, Recommendation Systems, Regression Models, YOLO, TrackNet, CNNs (VGG19, DenseNet121, ResNet50, MobileNetV2).
Data Analysis & Engineering
Exploratory Data Analysis (EDA), Data Pipelines, SQL, Data Scraping, Video Analytics, Spatio-temporal Analysis, Dataset Creation.