Rajendra Kumar
Senior Data Scientist & AI/ML Engineer
Experienced Senior Data Scientist with 5.5+ years specializing in machine learning, MLOps, and generative AI.
Proven track record of developing end-to-end ML solutions from research to production, with expertise in
predictive modeling, deep learning, and cloud platforms. Recently completed MS in Data Science with focus
on Big Data Systems at Indiana University, now leading AI/ML initiatives in healthcare analytics.
🎓 MS Data Science - Indiana University (2025)
📍 USA
💼 5.5+ Years Experience
💼 Professional Experience
Lead Data Scientist
Heartland Network, Chicago, IL
June 2025 – Present
- Lead AI/ML initiatives for healthcare analytics and clinical data science
- Develop predictive models for patient outcomes and operational efficiency
- Design and implement KPI dashboards for clinical and employment trend forecasting
Graduate Research Assistant
Indiana University, Bloomington, IN
September 2024 – May 2025
- Developed advanced machine learning models for healthcare data analysis and pattern recognition
- Built full-stack GenAI applications using LangChain, Streamlit, and modern ML frameworks
- Created RAG pipelines indexing 5K+ clinical patterns with semantic search capabilities
- Implemented Stable Diffusion workflows for medical imaging analysis and automated reporting
- Collaborated on interdisciplinary research projects in AI applications for healthcare and education
Senior Data Scientist
Target Corporation, Minneapolis, MN
April 2023 – June 2023
- Developed fraud detection models achieving 97% accuracy (0.97 ROC AUC) for payment processing systems
- Implemented Marketing Mix Modeling (MMM) for multi-channel attribution analysis
- Built Customer Lifetime Value (CLV) segmentation models for targeted marketing campaigns
- Created real-time dashboards and automated ETL pipelines reducing processing time by 40%
- Applied advanced analytics for inventory optimization and customer behavior analysis
Lead Data Scientist
Sutherland Global Services, Hyderabad, India
September 2018 – March 2023
- Led Propensity-to-Pay (P-T-P) modeling initiatives for revenue cycle management across healthcare sector
- Developed time-series forecasting models using PySpark and Azure ML for predictive analytics
- Built predictive models for customer churn and retention analysis using ensemble methods
- Implemented A/B testing frameworks for business process optimization and decision making
- Optimized query performance achieving 30% latency reduction and 50% memory efficiency improvements
- Managed cross-functional teams of 8+ data scientists and mentored junior analysts
- Developed automated reporting solutions using Python, SQL, and Tableau for stakeholder insights
- Implemented automated model deployment pipelines and monitoring systems for production ML models
- Performed statistical analysis and feature engineering for improving model performance
💡 Technical Skills
Programming Languages
Python (Expert)
SQL (Expert)
R (Advanced)
JavaScript (Intermediate)
Java (Intermediate)
Scala (Intermediate)
Bash/Shell
Machine Learning & AI
TensorFlow
PyTorch
Scikit-learn
LangChain
OpenAI APIs
Hugging Face
Stable Diffusion
MLOps
Deep Learning
NLP
Computer Vision
Time Series Analysis
Ensemble Methods
LangGraph
CrewAI
LangFuse
MCP
A2A
n8n
Cloud & Big Data Platforms
AWS (SageMaker, EC2, S3)
Azure (ML Studio, Databricks)
GCP (Vertex AI, BigQuery)
Databricks
Apache Spark (PySpark)
Hadoop
Apache Kafka
Docker
Kubernetes
Apache Airflow
Data Engineering & Databases
PostgreSQL
MySQL
MongoDB
Redis
Snowflake
Apache Hive
ETL/ELT Pipelines
Data Warehousing
Data Modeling
Analytics & Visualization
Tableau
Power BI
Matplotlib
Seaborn
Plotly
D3.js
Streamlit
Grafana
Jupyter Notebooks
Statistical Methods & Analytics
Statistical Modeling
A/B Testing
Hypothesis Testing
Causal Inference
Survival Analysis
Bayesian Statistics
Experimental Design
Feature Engineering
Customer Analytics
Marketing Mix Modeling
Development & Tools
Git/GitHub
CI/CD Pipelines
Model Deployment
API Development
Agile/Scrum
Project Management
Team Leadership
🎓 Education & Certifications
Academic Credentials
| Degree | Institution | Year | GPA |
|---|---|---|---|
| Master of Science in Data Science (Big Data Systems) | Indiana University Bloomington | 2023-2025 | 3.7/4 |
| PG-Diploma in Big Data Analytics & ML | Centre for Development of Advanced Computing | 2018-2018 | A |
| Bachelor of Engineering in Computer Science | RGPV Bhopal | 2013-2017 | 8.02/10 |
Professional Certifications
Relevant Coursework
- Machine Learning & Deep Learning
- Big Data Analytics & Statistical Computing
- Cloud Computing & Distributed Systems
- Natural Language Processing
- Computer Vision & Image Processing
- Data Mining & Knowledge Discovery
- Advanced Statistics & Experimental Design
- MLOps & Production Machine Learning
🚀 Featured Projects
🤖 Multi-Agent LLM System for Enterprise Analytics
Technologies: Python, LangChain, OpenAI GPT-4, Azure OpenAI, Streamlit, PostgreSQL
- Built advanced multi-agent system with specialized agents for data extraction, analysis, and reporting
- Implemented natural language querying capabilities enabling non-technical stakeholders to perform complex analytics
- Developed conversational AI interface with automated report generation and real-time insights
- Achieved 75% reduction in analysis time while improving data accessibility across teams
🎯 Dynamic Pricing Optimization Platform
Technologies: Python, TensorFlow, Apache Kafka, Redis, AWS SageMaker, Tableau
- Architected real-time pricing optimization system using reinforcement learning and Bayesian optimization
- Implemented dynamic pricing based on demand patterns, competitor analysis, and inventory levels
- Built real-time streaming pipeline with competitor monitoring and demand forecasting capabilities
- Increased revenue by 25% while maintaining optimal inventory turnover rates
🔍 Fraud Detection System with Explainable AI
Technologies: Python, TensorFlow, XGBoost, Apache Kafka, Elasticsearch, SHAP, LIME
- Developed production-grade fraud detection system processing millions of transactions daily
- Achieved 99.5% accuracy with ensemble methods combining XGBoost and neural networks
- Implemented SHAP and LIME for model explainability to meet regulatory compliance requirements
- Reduced false positive rate by 60% while maintaining high fraud detection accuracy
📊 Customer Lifetime Value Prediction Engine
Technologies: Python, XGBoost, LightGBM, Apache Spark, Azure ML, Docker, Kubernetes
- Developed end-to-end ML pipeline for CLV prediction using advanced ensemble methods and survival analysis
- Implemented real-time scoring system with automated model retraining and A/B testing framework
- Built behavioral segmentation models with clustering and statistical analysis techniques
- Increased marketing ROI by 40% and improved customer retention strategies
📈 Marketing Mix Modeling & Attribution Platform
Technologies: Python, PyMC3, Stan, R, Tableau, Google Analytics API, Facebook API
- Developed comprehensive marketing attribution models using Bayesian methods and causal inference
- Created unified measurement framework across online and offline marketing channels
- Implemented multi-touch attribution with incrementality testing and budget optimization
- Optimized media spend allocation resulting in 30% improvement in return on ad spend (ROAS)
🏥 Healthcare Analytics & Clinical AI Platform
Technologies: Python, PyTorch, Streamlit, RAG, LangChain, Stable Diffusion, FHIR API
- Built GenAI applications for clinical pattern recognition and patient outcome prediction
- Developed RAG pipelines indexing 5K+ clinical patterns with semantic search capabilities
- Implemented privacy-preserving machine learning techniques for sensitive medical data
- Improved patient outcomes by 20% and reduced operational costs by 15%
⏱️ Time-Series Forecasting & Revenue Analytics
Technologies: Python, PySpark, Azure, Prophet, ARIMA, LSTM, TensorFlow
- Developed advanced time-series forecasting models for revenue cycle management
- Implemented ensemble methods combining statistical approaches (ARIMA, Prophet) with deep learning (LSTM)
- Built automated model retraining pipeline with performance monitoring and alerting
- Optimized query performance achieving 30% latency reduction on Azure cloud platform
📸 Photo & Video Gallery
A collection of my educational and professional moments
Professional Experience at Sutherland Global Services
Appreciation at Target
College Performance - B.E. Days at University
Red FM Contest - Prize Winning Performance (B.E. Days)
RGPV Nodal Center Achievement
Target India Annual Meet