Open to AI/ML Opportunities

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12+ years applying statistical modeling, causal inference, and machine learning across federal, telecom, and financial sectors. Specializing in Agentic AI, RAG architectures, and end-to-end ML deployment.

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LangChain Agentic AI RAG LlamaIndex GCP / AWS MLOps
12+ Years Experience
85% Hallucination Reduction
$10M+ Business Impact

About Me

Data Science Manager with 12+ years applying statistical modeling, causal inference, and machine learning to drive data products in federal, telecom, and financial sectors. Expertise in end-to-end development and deployment of AI solutions including LLM-based architectures, RAG systems, forecasting models, and cohort and churn analyses.

Proven ability to partner with engineering, product, and sales teams to shape analytics strategy and deliver actionable insights — reducing hallucination risk by 85% and achieving 90% accuracy in fee forecasting.

🧠 Agentic AI & LLMs

RAG design, multi-agent orchestration, LangChain, LangGraph, LlamaIndex, CrewAI, AutoGen

🏢 Industries

Federal Government, Telecom, Healthcare, Hospitality, Financial Services

⚡ MLOps & Cloud

GCP BigQuery, AI Platform, AWS, 50+ Airflow DAGs, CI/CD, ML governance frameworks

📊 Leadership

Data Science Manager, team mentoring, executive stakeholder communication, analytics strategy

Technical Proficiency

Agentic AI & LLMs
Python / ML Frameworks
Statistical Modeling
Cloud & MLOps (GCP/AWS)
Data Visualization

Contact Info

(270) 421-3344
jsaimanoj@gmail.com
LinkedIn Profile
GitHub Profile

Featured Projects

🤖

Agentic RAG — Medical Literature System

LangChain Llama 3.2 Chroma Agentic AI

Engineered an autonomous multi-step RAG system capable of synthesizing 14,000+ pages of medical literature (~21K chunks). Implemented self-directed context grounding with Chroma vector database and automated testing pipelines.

85% Hallucination Reduction
21K+ Chunks Indexed
🏥

Medicaid A/B Testing & Experimentation

A/B Testing Causal Inference Statistics

Designed and executed a controlled A/B test for a national Medicaid client, doubling click-through rates and generating an estimated $8–10M in retained managed-care coverage value through data-driven experimentation design.

2x CTR (15% → 30%)
$10M Coverage Value
📈

Financial Fee Forecasting Platform

Time Series Ensemble ML Forecasting

Built high-precision predictive models for distinct financial portfolios using advanced time series methods and ensemble techniques, deployed across multi-domain resource and capacity planning challenges for federal clients.

90% Forecast Accuracy
$3.2M Net-New Revenue
📡

Verizon Dispatch Prediction Model

XGBoost Optimization Operations

Spearheaded a predictive dispatch reduction model at Verizon, classifying slow/moderate/fast dispatches to route jobs by efficiency. Improved accuracy 15% over baseline and significantly reduced operational costs.

25% Dispatch Reduction
+15% Accuracy vs Baseline

Technical Expertise

🧠

Agentic AI & LLMs

LangChain LangGraph LlamaIndex CrewAI AutoGen RAG LangSmith Chroma / Pinecone
🤖

Machine Learning

XGBoost Random Forest LightGBM Neural Networks Time Series Scikit-learn TensorFlow
📐

Statistical Methods

Causal Inference A/B Testing Hypothesis Testing Regression Analysis Clustering SMOTE
☁️

Cloud & MLOps

GCP BigQuery AWS Airflow Docker Spark CI/CD n8n / Retool
💻

Programming

Python SQL R Scala Bash PowerShell
📊

Data & Visualization

Tableau Power BI Plotly Matplotlib Hadoop ETL Pipelines

Skills Network

Interactive map of skills and projects — hover to explore connections, drag to rearrange

LLMs & AI Machine Learning Statistics Cloud & MLOps Programming Projects

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AI Playground

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Experience

Mar 2020 – Present

Data Science Manager

Deloitte Consulting LLP

  • Engineered an autonomous multi-step RAG system (LangChain + Llama 3.2) synthesizing 14,000+ pages of medical literature, reducing hallucination risk by 85%
  • Designed A/B test for national Medicaid client — raised CTR from 15% to 30%, generating $8–10M in retained managed-care coverage value
  • Built high-precision forecasting models for federal financial portfolios achieving 90% accuracy using ensemble time series techniques
  • Architected scalable data lake on GCP (BigQuery, AI Platform) and orchestrated 50+ Airflow DAGs, cutting ETL processing time by 60%
  • Engineered fraud risk models with 70% detection precision on highly skewed government datasets using SMOTE-augmented imbalanced learning
  • Delivered data-driven strategies generating $3.2M net-new revenue; established CI/CD practices and ML governance frameworks, accelerating time-to-production by 30%
Nov 2019 – Mar 2020

Data Analyst

Hilton

  • Implemented LightGBM Regressor with 90% accuracy to predict per-property revenue for real-time financial planning
  • Designed robust ARIMA forecasting model enabling real-time revenue predictions and strategic planning
  • Automated SQL pipelines for dataset creation, reducing data preparation time by 50%
Mar 2017 – Nov 2019

Statistical Data Analyst

Verizon

  • Developed predictive dispatch reduction model achieving 25% decrease in unnecessary dispatch hours, improving resource allocation across the logistics team
  • Improved model accuracy by 15% over baseline by classifying slow/moderate/fast dispatches for efficiency-based job routing
  • Executed A/B testing on design changes — 15% increase in sign-ups and 20% reduction in onboarding drop-off
  • Analyzed FiOS cancellation drivers using customer feedback analytics, improving satisfaction scores by 15%
Jul 2015 – May 2016

Research Data Analyst

George Mason University

  • Built classification ML models to identify optimal restaurant locations for Golden Corral and Ovation brands — identified 4 prime expansion locations
  • Developed predictive models for currency exchange rate forecasting using time series analysis
2012 – 2014

Data Analyst / Sr. Project Engineer

Regalix & IBM — India

  • Maximized AdWords campaign ROI by 20% through keyword refinement and data-driven campaign management at Regalix
  • Implemented SQL Server performance tuning and query optimization at IBM, improving system workload efficiency

Education & Certifications

Education

🎓

Post Graduate Certificate

Generative AI for Business Applications

The University of Texas at Austin

2026 (In Progress)
🎓

Master of Science

Data Analytics Engineering

George Mason University

2016
🎓

Bachelor of Technology

Computer Science Engineering

JNTU

2011

Certifications

☁️

Google Cloud Professional

Machine Learning Engineer
☁️

AWS

Machine Learning Specialty
📊

Tableau Desktop

Specialist
📊

Power BI

Data Analyst

Publications

Peer-reviewed research spanning AI, machine learning, and applied computer science — including construction risk management, generative AI applications, and drone-based crop health monitoring.

Let's Connect

Get In Touch

I'm always interested in discussing new opportunities, innovative AI/ML projects, and potential collaborations in data science and generative AI.

Phone

(270) 421-3344

LinkedIn

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