𝗜’𝗺 an 𝗲𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲𝗱 𝗠𝗟 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿 / 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝘁𝗶𝘀𝘁 with • 𝟭𝟬+ years of hands-on 𝗰𝗼𝗱𝗶𝗻𝗴 / 𝘀𝗼𝗳𝘁𝘄𝗮𝗿𝗲 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 foundations 🧱💻 • 𝟲+ years focused on 𝗿𝗲𝗰𝗼𝗺𝗺𝗲𝗻𝗱𝗲𝗿 𝘀𝘆𝘀𝘁𝗲𝗺𝘀 𝗥&𝗗 🧠⚙️ • a strong 𝗮𝗰𝗮𝗱𝗲𝗺𝗶𝗰 𝗿𝗲𝘀𝗲𝗮𝗿𝗰𝗵 background (𝗣𝗵𝗗 in Computer Science) 🎓🔬 Based in Melbourne 🇦🇺, I build 𝗲𝗻𝗱-𝘁𝗼-𝗲𝗻𝗱 data + ML products that ship — from messy signals to reliable production systems 🚀 ✨ 𝗪𝗵𝗮𝘁 𝗜 𝗱𝗼 (𝗲𝗻𝗱-𝘁𝗼-𝗲𝗻𝗱) 📥 Data/ETL → 🧱 Features → 🤖 Modeling → 🧪 Evaluation → 🛠️ Deployment → 📊 Monitoring → 🔁 Iteration 🧠 𝗠𝗟 / 𝗗𝗦 𝗲𝘅𝗽𝗲𝗿𝘁𝗶𝘀𝗲 • 𝗦𝘂𝗽𝗲𝗿𝘃𝗶𝘀𝗲𝗱 𝗠𝗟: classification/regression, calibration, imbalance ✅🎯 • 𝗦𝘁𝗮𝘁𝘀 & 𝗰𝗮𝘂𝘀𝗮𝗹 thinking: experiment design, inference, uncertainty 📐🧪 • 𝗧𝗶𝗺𝗲 𝘀𝗲𝗿𝗶𝗲𝘀: forecasting, anomaly detection, drift 📉⏱️ • 𝗡𝗟𝗣 / 𝗟𝗟𝗠 𝗮𝗽𝗽𝘀: RAG, tool-using agents, evaluation & guardrails 💬🧩🛡️ • 𝗥𝗲𝗰𝗦𝘆𝘀: retrieval → ranking → re-ranking, embeddings, real-time signals 🔎➡️📈➡️⚡ • 𝗥𝗲𝘀𝗽𝗼𝗻𝘀𝗶𝗯𝗹𝗲 𝗔𝗜: fairness/bias analysis + measurable trade-offs ⚖️🧠 🧰 𝗦𝗼𝗹𝗶𝗱 𝗰𝗼𝗱𝗶𝗻𝗴 + 𝗽𝗿𝗼𝗱 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 • Python / SQL 🐍🧾 • Spark / Databricks ⚡🏔️ • TensorFlow / PyTorch 🤖 • Clean architecture, testing, refactoring, code reviews ✅🧪🧹 • APIs, batch/stream patterns, performance & reliability 🛠️⚡ • MLOps: MLflow, reproducibility, model registry, CI workflows 🔁📦 • Observability: dashboards, alerting, monitoring & debugging 👀📟 📚 𝗥𝗲𝘀𝗲𝗮𝗿𝗰𝗵 & 𝗽𝘂𝗯𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 I’ve published 𝗵𝗶𝗴𝗵-𝗶𝗺𝗽𝗮𝗰𝘁 papers in 𝗠𝗟 / 𝗥𝗲𝗰𝗦𝘆𝘀 (and recently exploring 𝗟𝗟𝗠-related applications), with experience writing, reviewing, and translating research into real systems 📝🔬➡️🚀 🌱 𝗛𝗼𝘄 𝗜 𝘄𝗼𝗿𝗸 • Research-minded, product-aware, engineering-driven 🔬➡️📦 • Strong ownership: define the metric → ship the system → iterate 📊🚀🔁 • Clear communication: turn complexity into decisions 🗣️✅…𝗜’𝗺 an 𝗲𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲𝗱 𝗠𝗟 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿 / 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝘁𝗶𝘀𝘁 with • 𝟭𝟬+ years of hands-on 𝗰𝗼𝗱𝗶𝗻𝗴 / 𝘀𝗼𝗳𝘁𝘄𝗮𝗿𝗲 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 foundations 🧱💻 • 𝟲+ years focused on 𝗿𝗲𝗰𝗼𝗺𝗺𝗲𝗻𝗱𝗲𝗿 𝘀𝘆𝘀𝘁𝗲𝗺𝘀 𝗥&𝗗 🧠⚙️ • a strong 𝗮𝗰𝗮𝗱𝗲𝗺𝗶𝗰 𝗿𝗲𝘀𝗲𝗮𝗿𝗰𝗵 background (𝗣𝗵𝗗 in Computer Science) 🎓🔬 Based in Melbourne 🇦🇺, I build 𝗲𝗻𝗱-𝘁𝗼-𝗲𝗻𝗱 data + ML products that ship — from messy signals to reliable production systems 🚀 ✨ 𝗪𝗵𝗮𝘁 𝗜 𝗱𝗼 (𝗲𝗻𝗱-𝘁𝗼-𝗲𝗻𝗱) 📥 Data/ETL → 🧱 Features → 🤖 Modeling → 🧪 Evaluation → 🛠️ Deployment → 📊 Monitoring → 🔁 Iteration 🧠 𝗠𝗟 / 𝗗𝗦 𝗲𝘅𝗽𝗲𝗿𝘁𝗶𝘀𝗲 • 𝗦𝘂𝗽𝗲𝗿𝘃𝗶𝘀𝗲𝗱 𝗠𝗟: classification/regression, calibration, imbalance ✅🎯 • 𝗦𝘁𝗮𝘁𝘀 & 𝗰𝗮𝘂𝘀𝗮𝗹 thinking: experiment design, inference, uncertainty 📐🧪 • 𝗧𝗶𝗺𝗲 𝘀𝗲𝗿𝗶𝗲𝘀: forecasting, anomaly detection, drift 📉⏱️ • 𝗡𝗟𝗣 / 𝗟𝗟𝗠 𝗮𝗽𝗽𝘀: RAG, tool-using agents, evaluation & guardrails 💬🧩🛡️ • 𝗥𝗲𝗰𝗦𝘆𝘀: retrieval → ranking → re-ranking, embeddings, real-time signals 🔎➡️📈➡️⚡ • 𝗥𝗲𝘀𝗽𝗼𝗻𝘀𝗶𝗯𝗹𝗲 𝗔𝗜: fairness/bias analysis + measurable trade-offs ⚖️🧠 🧰 𝗦𝗼𝗹𝗶𝗱 𝗰𝗼𝗱𝗶𝗻𝗴 + 𝗽𝗿𝗼𝗱 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 • Python / SQL 🐍🧾 • Spark / Databricks ⚡🏔️ • TensorFlow / PyTorch 🤖 • Clean architecture, testing, refactoring, code reviews ✅🧪🧹 • APIs, batch/stream patterns, performance & reliability 🛠️⚡ • MLOps: MLflow, reproducibility, model registry, CI workflows 🔁📦 • Observability: dashboards, alerting, monitoring & debugging 👀📟 📚 𝗥𝗲𝘀𝗲𝗮𝗿𝗰𝗵 & 𝗽𝘂𝗯𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 I’ve published 𝗵𝗶𝗴𝗵-𝗶𝗺𝗽𝗮𝗰𝘁 papers in 𝗠𝗟 / 𝗥𝗲𝗰𝗦𝘆𝘀 (and recently exploring 𝗟𝗟𝗠-related applications), with experience writing, reviewing, and translating research into real systems 📝🔬➡️🚀 🌱 𝗛𝗼𝘄 𝗜 𝘄𝗼𝗿𝗸 • Research-minded, product-aware, engineering-driven 🔬➡️📦 • Strong ownership: define the metric → ship the system → iterate 📊🚀🔁 • Clear communication: turn complexity into decisions 🗣️✅WWWW…