Data Scientist | AI Engineer | Business Analytics Graduate Student @ Oklahoma State University
I am a cross-functional analytics professional with 3+ years of experience. I bridge the gap between technical AI implementation and strategic business intelligence, having managed large-scale operational data at Deloitte and won national championships in AI/Machine Learning.
| Role Focus | Mastery & Tools |
|---|---|
| 🤖 AI & Agentic Engineering | Agentic AI: LangGraph, Multi-agent orchestration, RAG Pipelines. LLMs: OpenAI, Anthropic, HuggingFace APIs. Vector DBs: Pinecone, FAISS. |
| 🧪 Machine Learning (ML) | Modeling: Random Forests, XGBoost, Ensemble methods. Forecasting: SARIMAX Time-Series, Clustering. Frameworks: Scikit-learn, TensorFlow, PyTorch. |
| 📊 Data Science & Analysis | Core: Python (Pandas, NumPy), SQL. Stats: Variance Analysis, Trend Identification, Statistical Testing. |
| ☁️ Cloud & Data Engineering | Platforms: GCP (Vertex AI, BigQuery), Snowflake, Azure. ETL/ELT: Alteryx, Automated Workflows, Data Modeling. |
| 📈 Business Intelligence (BI) | Visuals: Power BI, Tableau, Looker Studio. Insights: KPI Reporting, Reporting Automation, Stakeholder Management. |
- National Champion: Data4Good Datathon (Purdue University): Built a hybrid Python ensemble model pipeline on Azure using LLMs and Random Forests to classify unstructured survey text data, achieving 91% accuracy.
- Medicaid Enrollment Forecasting (University of Iowa): Developed SARIMAX time-series forecasting models to enable data-driven resource planning for insurance providers.
- Logistics Optimization (Groendyke Transport) : Applied clustering algorithms to 124,000 industrial segments of truck transport to identify operational patterns and support resource planning.
- Automated Analytics (Deloitte) : Managed datasets for 5,000+ miles of asset records and automated Python workflows, reducing data lag by 35%.
- LinkedIn: linkedin.com/in/srikarananand
- Email: fsrikar@okstate.edu
- Location: Irvine, CA