Hi, I'm Abjasree.

Lead Data Scientist (GenAI & Clinical AI) · 4+ Years · Innovator · Problem Solver

AI/ML researcher specializing in GenAI, Clinical NLP, and Computer Vision. I design and deploy intelligent pipelines that transform unstructured medical data into actionable insights, optimize workflows, and accelerate healthcare decision-making.

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📍 India · Open to Hybrid / Remote / On-site
✉️ abjasree1@gmail.com
📱 +91-90487-72210
🌟 Available for new opportunities

About

I'm a Lead Data Scientist with 4+ years of experience turning unstructured healthcare data into business-impactful solutions. Specializing in generative AI, LLMs, and clinical AI, I build scalable NLP and CV pipelines that enhance accuracy, reduce manual review time, and drive operational efficiency. My work spans from radiomics-based diagnostics to AI-powered document processing and therapy-outcome analytics, delivering measurable improvements in healthcare systems.

Skills

Languages & Frameworks

PythonSQLC++RMATLABPyTorchTensorFlowHugging FaceScikit-LearnLangChain

Cloud & MLOps

AWS SageMakerAWS S3Azure MLAzure FunctionsSnowflakeDockerCI/CD

AI/ML Domains

GenAI & LLMsClinical NLPComputer VisionRAGAI AgentsReinforcement Learning

Experience

DATYCS — Lead Data Scientist

Apr 2024 – Present · Remote

Led 6-member GenAI & Research squad; built AWS/Azure CI/CD (-80% model roll-out time: 3 weeks → 5 days).
Architected 3 transformer-based NLP/CV pipelines processing 10K+ AML/CLL charts into Snowflake (+35% throughput).
Fine-tuned TrOCR for handwritten prescriptions (+25% accuracy; real-time medication safety alerts).
Deployed Vision LLM on Azure for multi-handwriting prior-authorization forms (+28% accuracy; -45% manual review).
Leveraged autonomous AI agents for patient-specific search validation (+30% accuracy; -40% reply time).
Partnered with Eversana to analyze AML & CLL therapy-switch drivers, shaping pharmaceutical market-access strategies.

IIT Madras — Data Scientist

Aug 2022 – Mar 2024 · Chennai

Automated analysis of 1,000+ CT/MRI images (-80% manual preprocessing time).
Increased renal calculi & pulmonary nodule diagnostic accuracy by 18% using CNN + radiomics + Bayesian-optimized XGBoost.
Deployed QA models at Apollo Proton Cancer Center (-10% turnaround; flagged 12 high-risk plans/quarter).

Univ.AI — Teaching Assistant

Dec 2021 – Aug 2022 · Part-time

Assisted in teaching machine learning and data science courses to students from diverse backgrounds.
Mentored students in practical ML projects and helped them understand complex AI/ML concepts.
Contributed to curriculum development and course material preparation.

Projects

Radiomics for NSCLC Classification

Oct 2023 – Jan 2024

Built radiomics model with 83% accuracy for differentiating adenocarcinoma and squamous cell carcinoma in non-small cell lung cancer beyond nodule morphology in chest CT. Decision-curve analysis showed net clinical benefit across thresholds.

PublicationRadiomicsMachine LearningMedical Imaging

Zero-Shot Image Registration (ZSIR-FE)

Sept 2022 – Oct 2023 · Med-NeurIPS 2023

Designed DNN feature-based registration improving Dice score by +0.30 and running 6× faster than SIFT on BRaTs dataset through feature extraction approach.

Conference ProceedingsComputer VisionMedical Image Registration

Pulmonary Nodules Classification

Sept 2022 – Oct 2023

Engineered radiomic features + XGBoost on limited Indian dataset, achieving 89% accuracy (+10% vs prior studies) for pulmonary nodules classification from chest CT scans.

PublicationXGBoostFeature EngineeringMedical AI

Chat with My Portfolio Agent 🤖

August 2025

Passionate about creating interdisciplinary products powered by GenAI 🚀. For a quick tour of my experience and projects, chat with my agent 🤖.

AI AgentsHugging FaceGenerative AI

Automatic Detection of Renal Calculi

Sept 2022 – Jul 2023

Processed 163 CT scans and built deep-learning pipeline reaching 85% accuracy (+25 pp improvement); slated for clinical deployment.

Deep LearningMedical ImagingClinical AI

Elliptic flow of light nuclei

May 2019 - June 2020

Simulated heavy-ion collisions with AMPT; computed elliptic flow using STAR detector data for light nuclei. Developed C++ macros to compare Coalescence models with experimental measurements, achieving thesis distinction. Also worked on simple ML classification models to distinguish signals and background.

MS ThesisHPCC++Machine Learning

Education

Indian Institute of Science Education and Research, Tirupati

BS MS Dual Degree · CGPA: 8.0

2015 – 2020

G. H. S. S. Palakkad

Higher Secondary · Percentage: 97.33%

2013 – 2015

Jawahar Navodaya Vidyalaya (JNV)

High School · CGPA: 10

2012 – 2013

Contact

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