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Architecting Intelligence. Making Data Think.

Senior Data Scientist & AI Researcher bridging the gap between research and production reality. Exploring the full stack of Deep Learning, Data Science, and Autonomous Intelligence. Read my blogs.

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About Me

I began my journey not as a data scientist, but as a software engineer writing SQL frameworks in Delhi. That grounding taught me that data only speaks when it's properly structured. Over 12 years, I evolved from extracting and validating data to building machine learning models that forecast demand, predict churn, and segment customers with clinical precision.

Today, I live in Hannover, Germany — a city that mirrors my philosophy: calm, industrial-strength, and relentlessly innovative. Going forward, I will focus on partnering with European startups, scale-ups, and enterprises to turn their GenAI ambitions into secure, deployable, production-ready systems.

I'm also a lifelong learner — currently improving my German (B1 in progress) to better connect with the local ecosystem. From time to time I publish on GenAI, machine learning, deep learning, RAG, AI agents, and all things data science and AI.

RAG Architect

Multi-Agent Systems

MLOps

M.Sc. Data Science (SUNY)

Professional Journey

A track record of delivering high-impact AI solutions across diverse industries.

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Senior Data Scientist & AI Researcher

Remote Client Engagements

Aug 2023 – Present

Directing high-level research and architectural roadmaps for Generative AI. Engineered healthcare RAG pipelines cutting retrieval by 60% and multi-agent workflows yielding a 40% engagement lift.

monitoring

Senior Data Scientist

Impulsive Web Pvt. Ltd., India

Apr 2021 – Apr 2023

Developed an XGBoost-based predictive maintenance system reducing equipment downtime by 20%. Established MLOps standards (MLflow, dbt, Feast) and built a company-wide A/B testing framework accelerating deployment cycles by 50%.

insights

Data Scientist

Blue Horse Analytics, Sacramento, USA

Jul 2020 – Dec 2020

Developed demand forecasting models reducing late deliveries by 30% and transportation costs by 15%. Integrated AI insights into operations for 50% faster leadership decision-making.

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Data Scientist

Trinity Software Inc., South Salem, USA

Aug 2019 – Jun 2020

Built customer lifecycle models (RFM + K-Means) driving retention campaigns. Trained churn classifiers with 85% accuracy, reducing dropout rates by 25% through targeted interventions.

database

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analytics

Highlighted Projects

Exploring the boundaries of AI, Data Science, and interactive web experiences.

Featured Project

Healthcare RAG Knowledge System

Built a production-grade retrieval pipeline using LangChain, Pinecone, and chunk optimization over clinical databases. Result: 60% faster retrieval, 35% lower hallucination rate.

Generative AIPineconeLangChain

Featured Project

Multi-Agent Engagement Engine

Designed agentic workflows (LangGraph + CrewAI) that automated customer interaction analysis. Resulted in a 40% higher engagement and 25% user satisfaction improvement.

Multi-Agent SystemsLangGraphCrewAI

Featured Project

Enterprise A/B Testing Framework

Engineered a standardized experimentation stack with statistical guardrails. Slashed model deployment cycles by 50% and codified data-driven decision standards.

MLOpsStatisticsPython

Featured Project

Predictive Maintenance System

Developed an XGBoost-based predictive maintenance algorithm at Impulsive Web that accurately predicted failure modes, reducing equipment downtime by 20%.

XGBoostPredictive MaintenanceIoT

Featured Project

Transportation Demand Forecasting

Time-series models that cut late deliveries by 30% and saved 15% in logistics costs through predictive routing and capacity optimization.

Demand ForecastingTime SeriesLogistics

Featured Project

Customer Churn Prevention System

XGBoost classifier achieving 85% accuracy. Targeted campaigns driven by model insights reduced customer churn by 25%.

Customer LifecycleXGBoostAnalytics

Scientific Impact & Research Metrics

Validated outcomes delivered through rigorous data science and AI innovation.

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60% Faster

Clinical information retrieval (RAG)

trending_up

40% Uplift

Customer engagement via Multi-Agent AI

verified

35% Less

LLM hallucinations with Eval Frameworks

speed

50% Quicker

Model deployment via MLOps pipelines

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30% Reduction

Late deliveries using Demand Forecasting

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25% Retention

Churn reduction through ML interventions

psychology

9+ Mentored

Led teams & set organization MLOps standards

Featured Articles

Hand-picked reads on GenAI, MLOps, and the science behind building real AI systems.

Explore All Knowledge

Inquiry Portal

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