🚀 Senior Data Scientist | AI & Machine Learning Engineer | Product Manager
💡 Bridging Data Science, Engineering, and Business to Drive AI Strategy & Automation
📊 I build and deploy AI-driven solutions that optimize decision-making, automate workflows, and create measurable business impact.
With 5+ years in AI, data science, and analytics (12 including education), I build scalable, impact-driven solutions that integrate predictive modeling, NLP, optimization, and automation. From $1B+ growth reporting to GenAI platforms, and from core data science to Ivy League instruction, I turn complexity into actionable insights and impact across healthcare, energy, and finance.
--
- 🔍 Build AI Systems – Forecasting, optimization engines, invoice classification (80% accuracy, $100K+ savings)
- 🤝 Bridge Tech & Business – Leading cross-functional teams in DS, DE, and engineering to deliver enterprise AI
- 📊 Drive Analytics at Scale – Scaled platforms for Rx allocation, ad performance, and operational efficiency
- 🎓 Mentor at Scale – Taught 200+ students at Ivy League schools, guiding career transitions into DS/AI
- ✅ LLMs & GenAI – RAG, Agentic AI, LLM automation
- ✅ AI for Business – Strategy, decision science, ops optimization
- ✅ Data Science – ML systems for finance, supply chain, healthcare
- ✅ Coaching & Education – Empowering the next wave of DS leaders
- 💰 $100K+ saved | 25% faster marketing reports
- 🧠 Led DS/DE/Marketing teams for $1B+ growth campaigns
- 🤖 RAG/LLM pipeline → 80% accuracy, $100K+ saved
- 🛠️ Built optimization app for oil rigs
- 🎓 Led internal Analytics Academy training
- 🌱 20% increase in protective survey responses
- 🌐 Applied A/B testing + Random Forest for better resource targeting
- 🧪 LightGBM + EMD to predict waste volumes
- ♻️ Enabled smarter allocation strategies
- ⚖️ Built vision + NLP LLM for case analysis
- 📂 Improved legal document review and insights
- 📦 Linear programming engine for equitable allocation
- 🔄 Modeled demand/supply to guide distribution
- ⚙️ Developed agentic AI for predictive maintenance
- 🔍 Improved failure detection & uptime
- 💸 Drove $10M+ in sales via LLM-based automation
- 🧑🏫 Spearheaded GenAI training across the org
- 🚀 Boosted $10M+ revenue with DS solutions
- 📈 Enhanced data platforms for performance
- 🌍 Unified 100+ lab datasets into Azure Synapse
- 📊 Improved access to unstructured data
- 📉 $500K saved via RF & Monte Carlo forecasting
- 💼 $1M+ added revenue | Custom SQL driver for Google Sheets
- 🏫 Taught at Rice, Columbia, Northwestern, UW, Denver
- 📘 Topics: ML, MLOps, NLP, Forecasting, LLMs, Agents
Public Projects (WIP to revamp as I make my voice in this space more known!)
| Project Name | Description | Languages/ Tools Used | Repository Link |
|---|---|---|---|
| Customer Lifetime Prediction (BG/NBD Model) | This project leverages the BG/NBD (Beta-Geometric/Negative Binomial Distribution) model to analyze and predict customer purchasing behaviors. Using historical transaction data, the model estimates customer lifetime value and retention probability. | Python, Lifetimes, Pandas, Matplotlib | Project |
| ICO Viability Index | Analyzing various successful and unsuccessful ICOs for predictive success features. The project applies machine learning models and sentiment analysis to assess key factors contributing to ICO success. | Jupyter Notebook, Python, Random Forest, Neural Networks, Sentiment Analysis | Project |
| Multi-Chain GUI Wallet | A decentralized multi-chain GUI wallet with built-in atomic swaps and staking functionality, allowing users to seamlessly manage and transact across multiple blockchain networks. | Jupyter Notebook, Python, Blockchain, GUI Development | Project |
| Donchian Turtle Trading Strategy | This project implements and tests the Donchian Channel trading strategy using R. The strategy focuses on trend-following principles to optimize portfolio returns. The code is in an early experimental stage and requires further refinement. | R, Quantstrat, Financial Modeling | Project |
| FIPS Investment Decision Project | Leveraging US Census Bureau FIPS codes and area data to inform real estate investment decisions. The project analyzes socio-economic factors, food deserts, and neighborhood trends to create a data-driven investment strategy. | Jupyter Notebook, Python, Pandas, GeoPandas, Folium | Project |
| App Upsell Carousel A/B Test | A quick demo of an interactive A/B testing application built with Streamlit. The app helps visualize and analyze user engagement metrics for upselling in a mobile app. | Python, Streamlit, A/B Testing, Pandas | Project |
✅ Supervised & Unsupervised ML (Random Forest, XGBoost, LightGBM, SVM)
✅ Deep Learning (TensorFlow, PyTorch, CNNs, LSTMs, Transformers)
✅ Generative AI (LLMs, OpenAI, RAG, LangChain, Hugging Face)
✅ AI for Forecasting & Optimization (Time Series, Linear Programming, Heuristics)
✅ Big Data (SQL, Presto, Hive, Databricks, Azure Synapse)
✅ Data Processing (pandas, NumPy, Spark)
✅ Visualization (Power BI, Tableau, Matplotlib)
✅ AWS (SageMaker, Lambda, S3)
✅ Azure (ML Studio, App Services, Functions)
✅ Google Cloud (BigQuery, Vertex AI)
📩 Email: eccadena@gmail.com
🔗 LinkedIn: LinkedIn
📁 GitHub: GitHub
📝 X: X
Linktree - Additional Services Linktree
Thank you for visiting my GitHub! 😄
