Senior Associate, Data Analytics and Machine Learning

Location: Tuas, Singapore; this is an onsite role based at the Tuas manufacturing site. Transport is provided from Boon Lay, Kranji, and Outram Park MRT stations.

Company

hirify.global is a global eye-care company developing products and technologies that help people see brilliantly.

What you will do

• Develop and deploy data-driven solutions using machine, quality, MES, and SCADA data to improve manufacturing performance.
• Build predictive, prescriptive, and statistical models using production, quality, maintenance, and supply chain data.
• Analyze process variability and identify opportunities to improve OEE, yield, scrap reduction, throughput, and operational efficiency.
• Create manufacturing intelligence frameworks, executive dashboards, and operational control towers connected to real-time KPIs and predictive alerts.
• Develop MLOps pipelines for scalable deployment and model lifecycle management.
• Ensure solutions meet GxP, validation, cybersecurity, and data governance requirements while supporting replication across global manufacturing sites.

Requirements

• Bachelor’s or master’s degree in data science, AI, machine learning engineering, or computer science engineering.
• 3–8 years of hands-on experience in manufacturing data analytics or AI/ML development.
• Experience implementing smart factory solutions or industrial digital transformation initiatives.
• Comfortable being based onsite at the Tuas manufacturing site in Singapore.
• Ability to work effectively with manufacturing operations, process engineering, OT/IT systems, and cross-functional teams.

Nice to have

• Understanding of fast-paced GMP manufacturing environments.

Culture & Benefits

• Opportunity to contribute to hirify.global’s Lights-Out Manufacturing vision.
• Collaboration with cross-functional teams across manufacturing sites.
• Transport provided from Boon Lay, Kranji, and Outram Park MRT stations.
• Focus on inclusive teamwork and measurable impact on patients, customers, quality, productivity, and reliability.