Business Analyst | Data Scientist | ML Engineer

I'm passionate about turning messy data into clear business insights. Currently pursuing my Master's in Business Analytics at NUS, I love building predictive models, automating workflows, and solving real-world business problems with data.

I help businesses make smarter decisions through data. My work spans:

• Agentic AI & LLM Systems — Building multi-agent pipelines and intelligent tools that automate complex decisions, not just surface information
• Quantitative Modeling — Financial portfolio optimization, econometric demand analysis, ML-based risk scoring, and algorithmic trading across live markets
• Predictive Analytics — Forecasting sales, customer behavior, and business metrics using machine learning built for real production environments
• Business Intelligence — Dashboards and executive reports that translate complex data into decisions non-technical stakeholders can act on immediately
• Automation & Data Pipelines — ETL systems, no-code platforms, and workflow automation that eliminate the manual glue holding most data stacks together

• NSE Algo Trading System — Screens 2,400+ NSE stocks daily across 10 strategies with multi-checkpoint trade validation and end-to-end autonomous risk management
• Synpulse — Agentic AI for Chronic Disease — Multi-agent clinical decision support system built for the NUS-Synapxe-IMDA AI Innovation Challenge 2026
• IPL Hedge Portfolio Calculator — Real-time in-play betting optimizer using mean-variance, Kelly Criterion, linear programming, and Gemini AI for live match context
• Lead Qualification Platform — LLM-powered pipeline that improved enterprise client conversion rates by 40% through intelligent lead scoring and prioritization
• Loan Default Prediction — Business-cost-optimized ML model with a Streamlit dashboard that lets credit analysts explore risk scenarios without writing a line of code
• Industry Classification System — NLP model achieving 94% accuracy across massive datasets, deployed to categorize companies at scale for market intelligence
• E-commerce Warehouse Optimization — Linear programming model that minimizes fulfilment costs across a multi-warehouse distribution network

Languages & Dev: Python, R, SQL, PostgreSQL, JavaScript, FastAPI, Streamlit, REST APIs, Git/GitHub

AI & Data Science: LLMs, Agentic AI, Multi-Agent Orchestration, NLP, Prompt Engineering, Hugging Face, Sentence Transformers, TensorFlow, Generative AI

ML & Analytics: XGBoost, Regression (OLS, 2SLS, Panel), L1/L2 Regularization, K-Means Clustering, A/B Testing, Probabilistic Scoring, Statistical Modeling, SPSS

Quantitative Finance: Mean-Variance Optimization, Kelly Criterion, VaR, Sharpe/Sortino/Calmar Ratios, Backtesting, Technical Indicators (EMA, RSI, ATR)

BI & Visualization: Power BI, Plotly, Excel, Executive Dashboards, UML Diagrams

Automation & Platforms: N8N, Power Automate, Power Apps, ETL Pipelines, No-Code Workflows

• GitHub: You're already here! 🎉
• LinkedIn: linkedin.com/in/omgorakhia
• Email: om.gorakhia@u.nus.edu

I'm always open to interesting conversations about data, analytics, and building tools that actually help people make better decisions. Feel free to reach out!

Currently based in Singapore 🇸🇬 | Previously helped companies like Amazon, Walmart, and PayPal with data intelligence solutions