I lead a team of 8 Data Scientists that add business value through advanced modeling. My team and I build advanced statistical models, machine learning systems, and AI-powered tools that help organizations make data-driven decisions. My work focuses on causal inference, marketing mix modeling, LLM agentic systems, and deep learning for time series analysis.

SAGE - Strategic AI-Guided Explorer for Marketing Performance. An AI-powered copilot for Marketing Mix Modeling with natural language interface.

• 🤖 Agentic AI System with OpenAI function calling
• 💬 Natural language interface - ask questions in plain English
• 📊 Automatic visualizations - generates Plotly charts on demand
• 🎯 Budget optimization - SLSQP-based allocation across channels
• 🧠 RAG-powered insights - ChromaDB knowledge base with semantic search
• ⚡ Real-time analysis - instant answers to MMM questions

https://SAGEinsights.streamlit.app | 🚀 Live Demo | 📖 GitHub

llm-copilot - Production-ready agentic system for Marketing Mix Modeling with LLM orchestration, RAG, and code execution.

• 🎭 Agentic System: OpenAI function calling for tool orchestration
• 📚 RAG Implementation: ChromaDB + OpenAI embeddings for semantic search
• 🔧 Dynamic Code Execution: Safe Python sandbox for on-the-fly analysis
• 📈 Response Curve Fitting: Automatic Hill curve generation with deepcausalmmm
• 🗃️ Knowledge Base: Stores curves, benchmarks, glossary, and best practices
• 🔌 Multi-Database Support: 10+ connectors (Databricks, Snowflake, BigQuery, etc.)

pip install git+https://github.com/adityapt/llm-copilot.git Tech: OpenAI GPT-4, ChromaDB, LangChain patterns, Pandas, Plotly

Creator and maintainer of DeepCausalMMM - an open-source Python package for Marketing Mix Modeling with causal inference and deep learning.

• 🧠 GRU-based temporal modeling for automatic adstock and lag learning
• 🔗 DAG causal discovery to reveal channel interdependencies
• 📉 Response curves with Hill saturation for budget optimization
• 🌍 Multi-region modeling with learnable region-specific effects
• ⚡ Performance: 93% holdout R², 3.6% train-test gap

pip install deepcausalmmm📖 Documentation | 🎯 Quick Start | 📝 Examples

• Agentic system design with function calling
• Retrieval-Augmented Generation (RAG)
• Prompt engineering & Chain-of-Thought
• Vector databases (ChromaDB, Pinecone)
• LLM orchestration patterns

• Marketing Mix Modeling (MMM)
• Directed Acyclic Graphs (DAGs)
• Causal structure learning
• Treatment effect estimation
• Instrumental variables

• Time series forecasting
• Recurrent Neural Networks (GRU, LSTM)
• PyTorch model development
• Bayesian inference
• Ensemble methods

• Brand Science
• Marketing Mix Modeling
• Churn prediction
• A/B testing & experimentation
• Budget optimization
• Executive Insights

• SAGE AI Copilot (2025) - AI-powered MMM assistant with agentic system

  • Deployed on Streamlit Cloud
  • Natural language interface for marketing analytics
  • RAG-powered insights with ChromaDB

• LLM-Copilot (2025) - Production agentic system for MMM

  • OpenAI function calling orchestration
  • Dynamic code execution sandbox
  • Multi-database connectivity

• DeepCausalMMM (2025) - Advanced MMM with causal inference

  • Published on PyPI with 1.0.17+ versions
  • Comprehensive documentation on ReadTheDocs
  • 28 comprehensive tests with 100% pass rate
  • JOSS paper submitted

• ORCID: 0009-0008-9495-3932
• Focus areas: Causal inference, marketing analytics, LLM systems, time series modeling, Marketing mix modeling

• ✅ Built production agentic system with OpenAI function calling
• ✅ Implemented RAG with ChromaDB for semantic knowledge retrieval
• ✅ Created natural language interface for MMM analysis
• ✅ Integrated automatic budget optimization algorithms
• ✅ Deployed live Streamlit application
• ✅ Added 10+ database connectors (Databricks, Snowflake, etc.)

• ✅ Added non-linear response curves with Hill saturation

• ✅ Implemented proportional allocation for accurate scaling

• ✅ Enhanced Hill parameter constraints (slope ≥ 2.0)

• ✅ Integrated 14+ interactive visualizations

• ✅ Achieved 93% holdout R² with 3.6% performance gap

  • Installation: pip install deepcausalmmm
  • Documentation: deepcausalmmm.readthedocs.io
  • License: MIT (fully open source)
  • Python: 3.9+ compatible
  • Framework: PyTorch 2.0+

Currently exploring:

• LLM agent architectures and multi-agent systems
• RAG optimization techniques and hybrid search
• Transformer architectures for time series
• Causal discovery algorithms (NOTEARS, PC, GES)
• Bayesian deep learning for uncertainty quantification
• Multi-task learning for marketing applications
• Federated learning for privacy-preserving MMM

"Build AI/ML systems that are both theoretically rigorous and practically useful. Combine machine learning with causal inference to create tools that explain why, not just what."

I believe in:

• 🤖 AI for Good: Building LLM systems that augment human decision-making
• 📖 Open Science: Making research accessible and reproducible
• 📚 Documentation: Clear guides that help others learn and contribute
• 🤝 Community: Collaborative development and knowledge sharing
• 🎯 Impact: Solving real problems with elegant solutions

• 🤖 LLM agentic systems and RAG applications
• 🔬 Causal inference research and applications
• 📊 Marketing analytics and MMM projects
• 🧠 Deep learning for time series
• 📦 Open source data science tools
• 📚 Technical writing and documentation

Feel free to reach out if you're working on something interesting in these areas!