Joseff Ymanuel Tan

Collecting, scraping, validating, and enriching data from APIs, platforms, documents, and legacy systems to create reliable inputs for analytics and AI.

Designing scalable data pipelines that clean, deduplicate, transform, and structure data for analytics, automation, and machine learning at scale.

Building applied machine learning systems including NLP, computer vision, classification, prediction, and AI-powered workflows designed for real-world use.

Creating executive-grade dashboards and analytical views that surface clear, trustworthy insights and support confident decision-making.

Automating and orchestrating end-to-end workflows across systems to reduce manual work, improve reliability, and scale operations efficiently.

Working with IoT and real-time data streams to enable live monitoring, event-driven workflows, and time-sensitive analytics.

Building lightweight, data-driven applications when dashboards aren’t enough and users need interaction, control, or custom logic.

Integrating data, AI, and automation systems across cloud platforms and third-party tools to support secure, scalable, production-ready deployments.

Deploying machine learning models into production and monitoring performance, data drift, failures, and retraining needs over time.

Designing clean, intuitive interfaces and workflows that make data, analytics, and AI systems easy to understand and use.