4+ years as a software engineer, data engineer, or AI/ML practitioner writing technical design documents, data pipeline documentation, or API specifications
Technical Knowledge: Gain direct exposure & understanding of the semiconductor manufacturing test processes, at the same time learn how to handle big data to gain meaningful insights & build data visualization dashboards. Gain exposure to various applications leveraging on state-of-the-art digitalization/ AI methods that benefit test optimization.
Perform credit risk monitoring on portfolio to analyze risk trends and identify root causes; including origination, quality portfolio risk migration, performance vintage quality and system development.
Analyze moderation and labeling data from multiple queues to derive a process or market specific trend/correlation. Arrange supporting data points for impact analysis and solution planning.
Insights Generation - Analyze campaign performance, audience data, and digital channels to generate actionable insights that inform marketing strategies, targeting, and media optimization efforts.
Cross-Functional Prototyping: Collaborate with cross-functional teams to prototype innovative AI solutions and validate business value through proof-of-concepts.
Templating and configuration generation: Create and maintain configuration templates (Jinja2 or similar) that generate device-specific configs from structured inputs, ensuring consistency and reducing human error.
Develop and implement process automation, end‑to‑end automated data pipelines and workflows to improve efficiency, reduce manual effort, and enhance scalability of existing processes