---
title: "Manas Tiwari - Data Scientist 2 in Bengaluru | Tal"
description: "Meet Manas Tiwari, Data Scientist / ML Engineer | RAG, LLM Fine-tuning, MLOps | Python, PyTorch, AWS/GCP/Databricks | BITS Pilani based in Bengaluru. AI stack includes claude code, hugging face, and ollama. Explore public work links including github."
image: "https://tal.club/meet/manas-tiwari/share-image"
---

# Manas Tiwari on Tal

## Headline

Data Scientist / ML Engineer \| RAG, LLM Fine\-tuning, MLOps \| Python, PyTorch, AWS/GCP/Databricks \| BITS Pilani

## About

I'm a Data Scientist with 4\+ years building and productionizing ML/LLM systems at scale: spanning RAG pipelines, LLM fine\-tuning, and MLOps across AWS, GCP, and Databricks\. At CommerceIQ, I own the full lifecycle of our retrieval and classification systems: running RAG experiments across multiple LLMs and multimodal product data that lifted accuracy from 65% to 88%, cutting LLM inference latency by \~50% through an async pipeline redesign, and building the ingestion infrastructure that keeps our taxonomy's vector DB continuously fresh\. Before this, at Swift, I built forecasting and classification models \(route\-choice \+ quantile regression for delivery time estimation, image classifiers for weight verification, risk models for return prediction\) that moved real operational metrics — SLA hit\-rate, revenue per shipment, RTO rates\. I care about the full ML lifecycle, not just modeling: data and feature engineering, evaluation design, deployment, and the MLOps that keeps a model reliable in production\. Comfortable owning a problem end\-to\-end, from a messy dataset to a shipped API\. Currently exploring Data Scientist / ML Engineer / Applied Scientist roles where I can keep working across the LLM \+ production ML stack, and available to join immediately\. Open to connectin\. Reach me at tiwarimanas866@gmail\.com\.

## Location

Bengaluru

## AI stack

claude code

hugging face

ollama

## Work experience

Data Scientist 2 at CommerceIQ, Apr 2026 \- Present · 6 mos, Bengaluru · Hybrid

Data Scientist \- 1 at CommerceIQ, Aug 2024 \- May 2026 · 1 yr 10 mos, Bengaluru · Hybrid, \- Ran RAG experiments on Databricks across multiple LLMs and multimodal SKU metadata \(product description, bullet points, image\), lifting accuracy 65%→88%, coverage 43%→62%, and high\-confidence accuracy 70%→95%; cut LLM inference latency \~50% via an asynchronous pipeline redesign — a reduction that held even after upgrading to a larger model \(Gemini 2\.0 Flash → 2\.5 Flash\)\. \- Productionized an Airflow/AWS/Databricks ingestion pipeline that continuously refreshes our product taxonomy's vector DB, replacing a static index with dynamic updates and a feedback loop from manually corrected labels — improving retrieval accuracy by 5\-8 basis points\. \- Ran controlled fine\-tuning experiments on a BERT\-based classifier, achieving 88% accuracy across \~400 classes\. \- Constructed golden evaluation datasets via stratified sampling, replacing fully manual audits and cutting evaluation time from 1\-2 weeks to a few hours\. \- Built an error\-analysis module that shifted first\-level debugging of market\-share inaccuracies from Data Science to Operations, saving the DS team 2\-3 days/week\. \- Built a weekly accuracy/coverage reporting module based on Operations team annotations\.

Data Scientist at Swift, Jun 2023 \- Jul 2024 · 1 yr 2 mos, Bengaluru, \- Built a probabilistic ETD \(estimated delivery time\) model combining a route\-choice model with segment\-level quantile regression, achieving an 88% SLA hit\-rate\. \- Implemented a Multi\-Criteria Decision Making \(MCDM\) model for smart courier allocation, delivering a 20% improvement in seller platform performance\. \- Built an image classifier via transfer learning on EfficientNet \(TensorFlow\), achieving 93% accuracy — generating \~₹2 lakh/month in cost savings by resolving weight disputes\. \- Built an XGBoost\-based weight\-prediction classifier \(92% accuracy\), increasing revenue per shipment by ₹4\.5\. \- Developed a Multi\-Layer Perceptron model for RTO \(Return to Origin\) risk classification, achieving 83% accuracy / 74% precision and reducing seller RTO rate by 4%\. \- Optimized BigQuery queries, cutting monthly costs by 30%\. \- Managed a self\-hosted Kafka cluster with automated AVRO schema generation, cutting connector setup time by 3\-4 hours\. \- Built a jobserver orchestrating ETL and MLOps pipelines using async Python\.

Business Analyst at Swift, Jul 2022 \- Jun 2023 · 1 yr, Bengaluru, Karnataka, India

Business Analyst at Swift, Apr 2022 \- Jun 2023 · 1 yr 3 mos, Bengaluru

Intern at DHIO Research & Engineering Pvt Ltd\.,, May 2020 \- Jul 2020 · 3 mos, Mumbai City district, Maharashtra, India

## Education

Bachelor of Engineering, Mechanical Engineering, BITS Pilani, Hyderabad Campus

## Public links

- github: https://github\.com/glitch\-8

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

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