Data Engineer jobs in Singapore

One of our clients is seeking a skilled Senior Data Scientist to join their team and contribute to the development and enhancement of our AI/ML-based predictive maintenance platform. The ideal candidate will have a strong background in Machine Learning, Generative AI, MLOps experience with the Azure Data Stack, and a deep understanding of IoT data integration or Equivalent stack in AWS and Open platform.

Responsibilities:

• Model Development: Design and implement machine learning/ deep learning models for failure and anomaly predictions, utilizing time-series data from IoT devices.
• Stakeholder Communication: Effectively explain model training and inference logic, performance metrics, and decision boundaries to non-technical stakeholders, product owners, and fellow ML and data engineers.
• Performance Monitoring: Continuously monitor model performance and implement necessary adjustments to maintain accuracy and reliability.
• Platform Enhancement: Work closely with the product manager and technical lead to identify areas for improvement and implement enhancements in the predictive maintenance platform.
• MLOps & Pipeline Collaboration: Collaborate closely with MLOps engineers to iteratively build and enhance production-grade training and inference pipelines. Provide clear specifications, model requirements, and validation criteria to ensure the reproducibility, scalability, and reliability of ML workflows across environments.
• Data Integration: Collaborate with engineering teams to ensure seamless integration of IoT data, including analog/digital conversions, into the data pipeline.

Requirements:

• Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or a related field.
• Proven experience in developing, deploying, and monitoring machine learning models in production environments, preferably in predictive maintenance or time-series applications.
• Strong proficiency in Python and experience with ML frameworks such as TensorFlow, PyTorch, or Scikit-learn.
• Hands‑on experience with time-series modeling techniques
• Proficiency in Generative AI technologies, including prompt engineering, fine-tuning, and applying foundation models for predictive analytics and intelligent assistant features.
• Knowledge of transfer learning techniques to adapt pre-trained models to domain-specific predictive maintenance tasks.
• Hands‑on experience with Azure or AWS services, including IoT SDK, Event Hub, and Databricks.
• Familiarity with IoT devices and data collection methodologies, including analog/digital data processing.
• Excellent problem-solving skills and the ability to work collaboratively in a cross‑functional team environment.
• Experience with 3D visualization tools and integrating ML models into visual platforms preferable.