Neel Mishra

AI Researcher

Bengaluru, Karnataka, India5 yrs 3 mos experience

AI EnabledAI ML Practitioner

Key Highlights

Expert in Graph Neural Networks and Generative AI.
Proven track record in enhancing recommendation systems.
Significant contributions to LLM fine-tuning and chatbot development.

Stackforce AI infers this person is a Data Science and AI specialist with expertise in E-commerce and Automotive sectors.

Contact

Skills

Core Skills

Graph Neural NetworksGenerative AiData ScienceLarge Language Models (llm)Chatbot DevelopmentMachine LearningPredictionHypothesis TestingStatistical AnalysisNumerical OptimizationGenerative Adversarial Networks (gans)

Other Skills

Customer Relationship Management (CRM)Applied Machine LearningHierarchical ClusteringReinforcement LearningRLHFRetrieval-Augmented Generation (RAG)AerospikeUnderstand & Convey Complex InformationArchitectureProduct DemonstrationAnalytical SkillsTimelinesRequirements GatheringBusiness MetricsApplication Programming Interfaces (API)

About

Applied Scientist 2 @ Microsoft

Experience

5 yrs 3 mos

Total Experience

1 yr 6 mos

Average Tenure

8 mos

Current Experience

Microsoft

Applied Scientist 2

Sep 2025 – Present · 8 mos · Bengaluru · Hybrid

Building heterogeneous knowledge graph representations to propagate verdicts across multiple indicators (apps, IPs, emails, sessions), improving threat attribution accuracy.
Driving the design and implementation of graph-based threat detection and explainability systems using GNN architectures (GraphSAGE, GCN), risk propagation techniques, and embedding-based models.
Leading the development of scalable, low-latency Azure Synapse data pipelines to productionize machine learning models for real-time threat detection and response.

Graph Neural NetworksGenerative AI

Myntra

Data Scientist

Sep 2024 – Jul 2025 · 10 mos · Bengaluru · Hybrid

Created and deployed the Maya GenAI chatbot, driving a 58.86% increase in engaged MAU and a 31.74% improvement in repeat usage through a multi-agent architecture with dedicated personalization and search agents.
Enhanced Myntra’s recommendation system, achieving 0.81 nDCG and 0.61 MAP@15 for personalized search by fine-tuning the CLIP model on 1.4M image–description pairs.
Built scalable PySpark pipelines to train Word2Vec embeddings on 500M+ records, leveraging custom evaluation metrics to reduce embedding dimensionality from 100 to 22 while preserving semantic quality.

Data ScienceLarge Language Models (LLM)

Tekion corp

Data Scientist

Jul 2023 – Sep 2024 · 1 yr 2 mos · Bengaluru · Hybrid

Fine-tuned LLM models tinyLlama, Llama2, and Zephyr to enhance the email generation process, improving user experience for multiple automotive dealerships.
Achieved a 98.08% reduction in execution time for fine-tuned LLMs through advanced techniques like paged attention and post-training quantization.
Conducted advanced statistical analysis to validate the improved performance of LLMs, achieving 21% higher similarity and 35% increased relevance in generated emails.
Developed a robust RAG question-answering chatbot for vehicular marketing, processing comprehensive data for over 800 car models using Langchain and advanced features.

PredictionHypothesis Testing

International institute of information technology hyderabad (iiith)

Research Assistant

Nov 2020 – Jun 2023 · 2 yrs 7 mos · Hyderabad · On-site

Collaborated with Principal Applied Scientists at Microsoft India to develop a minimax optimizer for GANs, significantly improving inception scores while reducing execution time to 0.184 seconds.
Implemented GPU-accelerated PyTorch solvers for Conjugate and Bi-Conjugate Gradient methods, achieving over 96% runtime reduction compared to standard NumPy implementations.
Authored the paper “Angle based learning rate for gradient descent,” presented at the International Joint Conference on Neural Networks (IJCNN 2023, Rank A), proposing a novel learning rate adaptation method.
Authored the paper “A Gauss-Newton Approach for Min-Max Optimization in GANs” under the Microsoft Academic Partnership Grant (MAPG), improving GAN optimization efficiency using the Gauss-Newton method (WCCI 2024).

Numerical OptimizationGenerative Adversarial Networks (GANs)