---
title: "Sayan Chaudhuri - Data Scientist in Bengaluru | Tal"
description: "Meet Sayan Chaudhuri, Data Scientist | ML Engineer | Deep Learning · Computer Vision · Physics-Informed ML | PyTorch · Azure · AWS | IISc based in Bengaluru. AI stack includes claude, cursor, and langchain. Explore public work links including github and leetcode."
image: "https://tal.club/meet/sayan-chaudhuri/share-image"
---

# Sayan Chaudhuri on Tal

## Headline

Data Scientist \| ML Engineer \| Deep Learning · Computer Vision · Physics\-Informed ML \| PyTorch · Azure · AWS \| IISc

## About

My path into ML started at IISc, where my thesis on robust object detection pulled me toward a specific kind of problem: models that have to hold up when real\-world conditions stop cooperating\. At Mercedes\-Benz R&D, I built physics\-informed deep learning systems for EV battery thermal prediction, integrating thermodynamic constraints directly into loss functions so models respected the physics, not just the data\. I also worked on VAE\-based anomaly detection for battery lifecycle monitoring across vehicle fleets, and LLM\-powered document automation using RAG and LangChain\. Co\-inventor on 2 filed global patents from this work\. At Picarro, I build ML systems where being wrong has real consequences\. I replaced a 15\-second classical solver with a 10ms deep learning inference pipeline for real\-time spectroscopic gas analysis, and built an atmospheric source inversion system using Bayesian MCMC over physics\-based dispersion models to localize industrial emissions with quantified uncertainty\. My IISc thesis focused on robust object detection under real\-world visual corruptions — improving Mask R\-CNN performance under distribution shift through custom augmentation and regularization\. Published at ETAAV 2025\. 2 filed global patents\. 1 published paper\. M\.Tech, IISc Bangalore\. Looking for roles where research depth and the discipline to ship both matter\.

## Location

Bengaluru

## AI stack

claude

cursor

langchain

mcp

notebooklm

ollama

## Work experience

Data Scientist at Picarro, Aug 2025 \- Present · 1 yr 1 mo, Bengaluru, Karnataka, India, Building deep learning systems for real\-time spectroscopic gas analysis and industrial atmospheric monitoring — from model architecture through production deployment on AWS\. Work spans physics\-informed deep learning, Bayesian inference, distributed GPU training, and MLOps\. • Replaced a 15\-second classical optimization model with a 10ms deep learning inference pipeline for real\-time gas concentration measurement; improved compound detection accuracy from 78% to 84% using a 1D Vision Transformer with compound query cross\-attention on infrared spectroscopic data\. • Generated 200M synthetic spectral mixtures via physics\-based simulation and trained across 4 GPUs \(AWS g4dn\.12xlarge\), cutting training time from 26 hours to 2\.5 hours; validated model performance against real\-world sensor data\. • Built an atmospheric source inversion system for industrial fenceline monitoring — applied Bayesian MCMC over physics\-based dispersion models to localize emission sources and quantify uncertainty from continuous multi\-sensor data\. • Designed a multi\-source emission identification approach using sparse regularization on a physics\-based forward model dictionary, recovering active source locations among an unknown number of simultaneous emitters without requiring prior knowledge of source count\. • Established MLflow\-based experiment tracking and model monitoring with KPIs covering prediction drift, data quality, and production model performance\. • Present model performance and business impact in quarterly business reviews to technical and executive stakeholders\.

Junior Data Scientist at Mercedes\-Benz Research and Development India, Aug 2023 \- Jul 2025 · 2 yrs, Bengaluru, Karnataka, India, Built end\-to\-end ML pipelines on Azure for EV battery systems — spanning physics\-informed model training, anomaly detection, generative AI, and production API deployment\. Co\-inventor on 2 filed global patents from this work\. • Designed and deployed a physics\-informed deep learning model for EV battery thermal prediction — integrated thermodynamic constraints directly into loss functions to improve physical consistency across operating conditions, trained on synthetic data and validated against real vehicle sensor readings\. • Developed VAE\-based anomaly detection for battery lifecycle monitoring across multiple vehicle models, enabling early detection of degradation patterns from fleet telemetry\. • Built a RAG pipeline using LangChain and GPT\-based models with FAISS vector search for automated document summarization, reducing manual review from hours to minutes\. • Developed a FastAPI backend for real\-time EV route optimization — integrated charging station data from PostgreSQL, implemented round\-robin load balancing across Azure Functions, and served fleet\-level inference requests\. • Deployed models to production via MLflow and Azure ML with CI/CD pipelines, collaborating with product managers and software engineers across the full development cycle\.

M\.Tech Researcher at Indian Institute of Science \(IISc\), Aug 2021 \- Jul 2023 · 2 yrs, Bangalore Urban, Karnataka, India, Thesis research at IISc's Artificial Intelligence and Robotics Laboratory on domain generalization for instance segmentation in aerial imagery — building deep learning models resilient to the sharp performance drops caused by real\-world shifts in lighting, weather, and atmospheric conditions\. • Developed Metacognitive Mask R\-CNN in PyTorch — integrated a self\-adaptive binary loss selection mechanism and SpatialDropout2D regularization into a ResNet\-101 \+ FPN backbone, enabling robust learning from just 455 annotated aerial images\. Achieved mAP 69\.3 on NWPU VHR\-10, outperforming CPISNet \(67\.5\), ARE\-Net \(64\.8\), and Precise Mask R\-CNN \(64\.8\)\. • Systematically evaluated five augmentation strategies \(APR, GridMask, Random Erasing, Gaussian Blur, Color Jitter\) and three dropout variants \(SpatialDropout2D, DropBlock, Standard Dropout\) through full ablation studies — SpatialDropout2D combined with APR delivered the strongest robustness, improving detection by 9\.5% and segmentation by 6\.5% under extreme brightness corruption versus baseline\. • Published at IEEE ETAAV 2025 — equal first\-author contribution

## Education

Master of Technology \- MTech, Aerospace Engineering, Indian Institute of Science \(IISc\)

Bachelor of Technology \- BTech, Electrical Engineering, BIT Sindri

## Public links

- github: https://github\.com/sayanchaudhuri14
- leetcode: https://leetcode\.com/sayanchaudhuri

[View the original profile](https://tal.club/meet/sayan-chaudhuri)

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