Job description
Hudson is partnering with a major manufacturing multinational firm in Singapore to recruit a Data Scientist focused on Smart Engineering. This is a rare opportunity to influence production strategy across global operations by applying advanced analytics, machine learning, and AI to real-world manufacturing challenges. You will work with diverse data sources, including IIoT sensor streams, MES and ERP data, and quality metrics to uncover insights that improve yield, reliability, and energy efficiency. Embrace Industry 4.0 principles as you design data-driven solutions that scale across sites, and collaborate with engineering, IT, and operations to turn insights into action. The role offers a collaborative, employee-centric culture with strong emphasis on learning, innovation, and career growth within a leading multinational environment.
The ideal candidate is a hands-on data scientist who can navigate the entire analytics lifecycle—from data wrangling and feature engineering to model development, deployment, and monitoring in production. You will apply time-series analysis, anomaly detection, predictive maintenance, and optimization techniques to drive measurable improvements in throughput, quality, and uptime. Expect exposure to digital twins, cloud-based analytics, streaming data platforms, and modern visualization tools while maintaining rigorous governance and documentation.
We are seeking a proactive, results-driven professional with a passion for manufacturing analytics who communicates clearly with technical and non-technical stakeholders. If you thrive in a fast-paced, globally connected environment and want to help shape the next generation of smart manufacturing, this role offers a compelling platform to advance your career.
Responsibility
- Build and deploy predictive maintenance models for critical equipment and processes to reduce downtime and maintenance costs.
- Create digital twins of production lines to simulate, optimize throughput, and improve product quality.
- Analyze streaming IIoT sensor data, SPC and OEE metrics to uncover performance gaps and drive improvements.
- Collaborate with engineering, operations, and IT teams to translate data insights into actionable business actions.
- Design and develop dashboards and self-serve analytics for executives and shop floor teams using Power BI or Tableau.
- Lead experiments, A/B testing, and design of experiments to optimize process parameters and yield.
- Establish robust model governance, monitoring, and documentation to ensure reliability in production.
Qualification
- Bachelor's or Master’s degree in Data Science, Computer Science, Engineering, or a related field.
- 3+ years of hands-on data science experience; manufacturing or industrial analytics is a plus.
- Proficiency in Python (pandas, scikit-learn) and SQL; experience with ML ops and containerization (Docker/Kubernetes) is a plus.
- Experience with time-series analysis, predictive maintenance, IoT data, and data visualization tools (Power BI, Tableau).
- Strong knowledge of manufacturing processes, quality metrics (SPC, OEE) and process optimization.
- Excellent communication and collaboration skills with the ability to influence stakeholders at all levels.