Headline

Data Scientist @ Meesho | Ex-Amazon Applied Scientist Intern | Applied ML, Personalization & Causal ML | LLMs & GenAI | IIT (BHU)

About

I am a Data Scientist at Meesho and an IIT (BHU) graduate, working at the intersection of Applied ML, personalization, causal ML, and LLMs. At Meesho, I build production ML systems for User Growth, including contextual bandits, uplift modeling, personalized discounting, and real-time model serving. My work has contributed to a 9% ODNR uplift across the discounting and personalization charter. Previously, as an Applied Scientist Intern at Amazon, I worked on LLM-based Sponsored Ads ML, including 7B+ parameter model fine-tuning, Mixture-of-Experts architectures, PEFT, distributed training, and large-scale inference. My work included a 137 bps recall improvement and ML systems supporting 1B+ daily Query-Ad inferences. Core areas: Applied ML · LLMs/GenAI · Deep Learning · ML Systems · Deep Learning · Personalization & Recommender Systems · Causal ML · Contextual Bandits I’m currently exploring Applied Scientist, ML Engineer, and Data Scientist opportunities focused on high-impact ML problems from experimentation and modeling through production.

Location

Bengaluru

AI stack

claude code

claude

codex

cursor

hugging face

openai

Work experience

Data Scientist at Meesho, May 2025 - Present · 1 yr 5 mos, Bengaluru, Data Scientist 1 — User Growth, Meesho • Drove a 9% ODNR uplift across the discounting & personalization charter by building personalized discounting using constrained optimization and heterogeneous treatment-effect modeling. • Improved personalization across 6 PLP/PDP surfaces by replacing static rules with contextual bandits (Thompson Sampling / LinUCB), enabling adaptive card selection based on user context and delayed rewards. • Designed production-grade online decisioning and inference, serving ML models through NVIDIA Triton and TensorRT while maintaining online updates to bandit posteriors for real-time personalization. • Built end-to-end ML data infrastructure using Spark, Databricks, and Airflow for feature engineering, reward attribution, and daily offline feature-store (OFS) tables powering production models. • Established offline experimentation and model validation frameworks using AUUC, Qini coefficient, uplift/distribution analysis, and bandit traffic/posterior diagnostics to assess incremental impact, convergence, and stability before production rollout

Applied Scientist at Amazon, May 2024 - Aug 2025 · 1 yr 4 mos, Bengaluru, Applied Scientist Intern & Return Intern — International Machine Learning, Amazon • Improved Sponsored Ads relevance by 4.2%+ ROC-AUC by developing and fine-tuning transformer-based models including BERT, LLaMA, Mistral, Mixtral, and Falcon for relevance. • Improved recall by 137 bps by designing MoE-BERT, a Mixture-of-Experts architecture that increased model capacity by 16× while maintaining similar latency, with custom routing, gating, and expert architectures in PyTorch. • Scaled fine-tuning of 7B+ parameter LLMs across 10 marketplaces, leveraging distributed training and parameter-efficient techniques including LoRA, QLoRA, and Flash Attention. • Improved model efficiency and serving economics through INT8/4-bit quantization while maintaining ROC-AUC within tolerance, and explored test-time memory modules for improved long-context retention. • Evaluated LLM adaptation strategies across 8+ regional languages, benchmarking Zero-/One-/Few-Shot and Adaptive In-Context Learning alongside prompt and fine-tuning approaches. • Contributed to ML systems serving 1B+ daily Query-Ad inferences across Amazon’s e-commerce sites and applications, balancing model quality, inference efficiency, and production scalability.

Amazon ML Summer School at Amazon, Sep 2023 - Aug 2025 · 2 yrs, India, Apprenticeship

Core Member at Club of Programmers, IIT BHU, Sep 2022 - Jan 2025 · 2 yrs 5 mos, Varanasi, Uttar Pradesh, India, 𝗖𝗼𝗿𝗲 𝗠𝗲𝗺𝗯𝗲𝗿: 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 𝗚𝗿𝗼𝘂𝗽 ● Part of the Core Team actively responsible for all the AI and ML initiatives in the Institute.

Marketing Executive at Kashiyatra, IIT (BHU) Varanasi, Dec 2021 - Nov 2022 · 1 yr, Varanasi, Uttar Pradesh, India

Strategic Relations , E-Cell IIT(BHU) Varanasi at E-Cell IIT BHU, May 2021 - Jan 2023 · 1 yr 9 mos, Varanasi, Uttar Pradesh, India

Education

Integrated Dual Degree(B.Tech + M.Tech), Indian Institute of Technology (Banaras Hindu University), Varanasi

Integrated Dual Degree (B.Tech + M.Tech), Metallurgical Engineering, Indian Institute Of Technology(BHU),Varanasi

Public links

codeforces: https://codeforces.com/profile/dopamIND_21