RUSHIKESH HIRAY
Senior Data Scientist & ML Engineer
I design production ML systems for acquisition, segmentation, computer vision, GenAI, and optimization, turning complex data into decisions teams can trust and use at scale.
About
Production-grade machine learning systems for scoring, segmentation, detection, retrieval, and optimization.
Featured Client Work
Banking & Risk
Computer Vision
Autonomous Driving
GenAI & LLMs
Optimization
Experience
Career progression and impact
Sr Lead Data Scientist
HDFC Bank
- Built end-to-end Business Loan and Personal Loan acquisition models using XGBoost and CatBoost.
- Engineered 300+ features across CASA, Credit Card, Digital Funnel, and Loan One-View datasets.
- Scaled PySpark and SQL pipelines for 2 crore+ customers across 18 source systems and leadership-facing dashboards.
- Improved top-2 decile capture from roughly 60% to 75%+ for acquisition campaigns.
Senior Data Scientist
iLink Digital
- Built the Pipe Insight defect detection pipeline for AECOM using Faster RCNN-based inspection models.
- Developed a RAG-powered financial document assistant for Abdiel Capital Advisors, LP.
- Improved annotation tooling and OCR post-processing workflows to raise data quality and downstream model usefulness.
- Reduced manual inspection effort for infrastructure workflows through automated defect detection.
ML Engineer - Computer Vision
Tata Consultancy Services
- Built YOLOv4 object detection pipelines and DeepSORT tracking for real-time driving scenes.
- Developed traffic sign classification and DeepLabV3 lane extraction models in TensorFlow.
- Created internal annotation tooling in PyQt5 for image and LiDAR labeling workflows.
- Reached 92 mAP at 0.7 IOU for object detection in autonomous driving use cases.
Optimisation Engineer
Tata Consultancy Services
- Built a vehicle testing scheduler for Nissan using Microsoft Z3 SAT Solver and constraint programming.
- Modeled vehicle, test, and dependency rules directly into mathematical decision logic.
- Combined exact constraints with greedy optimization for practical schedule generation speed.
- Reduced testing schedule implementation cost by 40%.
Selected Projects
Technical depth and real-world applications
ML-Driven Loan Acquisition Engine
An acquisition decision engine for Business Loan and Personal Loan campaigns targeting existing-to-bank customers.
Framed campaign prioritization as a supervised ranking and classification problem using XGBoost and CatBoost.
Improved top-2 decile capture from roughly 60% to 75%+.
Customer Risk Categorization and Segmentation
A customer segmentation and recommendation foundation used to categorize risk and improve product relevance at scale.
Built Autoencoder plus KMeans segmentation over 150+ variables to uncover actionable customer clusters.
Improved Business Loan recommendation lift by 8-10x over business-as-usual baselines.
Pipe Insight Defect Detection
An automated inspection pipeline for identifying pipeline defects from imagery instead of relying only on manual review.
Built Faster RCNN-based detection models for defect localization and classification.
Reduced manual inspection effort by automating defect identification at scale.
Autonomous Driving Perception Stack
A perception workflow combining object detection, tracking, and lane segmentation for autonomous driving scenarios.
Built YOLOv4 object detection models for dynamic road scenes.
Achieved 92 mAP at 0.7 IOU for object detection.
Vehicle Testing Schedule Optimizer
A scheduling engine that transformed complex prototype vehicle testing constraints into faster planning decisions.
Modeled tests, vehicles, and dependencies as constraint satisfaction problems using Microsoft Z3 SAT Solver.
Reduced implementation cost by 40%.
RAG Assistant for Financial Research
A retrieval-augmented assistant that let analysts ask natural-language questions over financial documents.
Built a document retrieval and LLM answering workflow over financial research materials.
Accelerated research workflows with conversational access to financial documents.
Skills & Stack
Technical breadth across ML, engineering, and systems
Predictive Modeling
Build ranking and classification systems that improve targeting, acquisition, and customer prioritization.
Data and Feature Engineering
Turn fragmented source systems into stable, decision-ready feature pipelines for large-scale ML programs.
Computer Vision
Deliver perception systems for inspection and autonomous-driving style environments where model quality must survive messy real data.
GenAI and RAG
Create retrieval-grounded AI workflows that help users interrogate documents and domain knowledge safely and efficiently.
Optimization and Decision Systems
Model operational constraints directly so the system recommends better schedules, allocations, and actions.
Education
Academic foundation and credentials
University of Pune
Bachelor's Degree — Computer Science
2014 - 2018
Certifications & Achievements
- Deep Learning Specialization
- Professional Scrum Master
- Best Innovation Award - National Level Competition
- Smart India Hackathon Participant
GitHub
Open source work and technical portfolio
@rhiray1996
Engineering and public-build proof of ML work.
Actively building new projects in GenAI, RAG Systems, and ML Pipelines.
Resume
Download or preview my full resume
Rushikesh Hiray — Resume
A concise overview of my experience, skills, and background in machine learning and data science.
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Open to opportunities, collaborations, and conversations