Data Scientist, AI/ML Predictive Maintenance Platform

Keppel Data Centres; Singapore; On-site; SGD 90,000 - 150,000; Full time

Job summary

Keppel Data Centres in Singapore seeks a skilled Data Scientist to help shape the next-generation AI/ML-based predictive maintenance platform. You will apply classical and Gen AI techniques to real-time IoT signals across diverse assets, delivering production-ready models with rapid iterations.

You will own experiments, collaborate with product managers and engineers, and balance accuracy with deployment cost and explainability while building monitoring and retraining strategies.

Responsibilities

• Model Development for time-series sensor data across assets.
• Train and fine-tune models for anomaly detection and failure prediction.
• Explore forecasting, anomaly, change point, survival analysis, and representation learning techniques.
• Apply hybrid approaches combining thresholds with ML models.
• Deliver working models into dev/production in 1–2 week sprints.
• Monitor model performance, drift, and recall in production.
• Plan retraining and rollback strategies for data/model drift.
• Use dashboards/alerts to track live model health.
• Collaborate with product managers, engineers, and stakeholders.

Skills

Model Development

Time-series modeling

Anomaly detection

Forecasting

Change point detection

Survival analysis

Representation learning

LSTM/Transformer

MLOps

Experimentation

Job description

Job Description

We are seeking a skilled and pragmatic Data Scientist to join our team in shaping the next-generation AI/ML-based predictive maintenance platform. You’ll work on applying both classical and Gen AI techniques to real-time IoT signals across diverse physical assets. If you thrive in fast iterations, own your experiments, and believe in getting things to production—not just Jupyter notebooks—this role is for you.

Key Responsibilities

• Model Development
• Develop, train, and fine-tune models on time-series sensor data for both anomaly detection and failure prediction across various asset types.
• Explore and apply a mix of forecasting, anomaly detection, change point detection, survival analysis, and representation learning techniques, choosing the best fit based on the use case.
• Be comfortable applying hybrid approaches (e.g., combining statistical thresholds with ML models, or chaining changepoint detection with LSTM/Transformer predictors) when appropriate.
• Iterate quickly and deliver working ML models into dev or production environments every 1–2 week sprint, enabling rapid feedback and continuous improvement.
• Balance performance, explainability, compute cost, and deployment constraints in every modeling decision
• Model Performance Monitoring
• Continuously monitor model performance in production (e.g., accuracy, drift, recall).
• Build a retraining and rollback strategy to handle data drift, model drift or edge cases.
• Use dashboards or alerts to track live model degradation.
• Proactively recommend model retirement or replacement.
• Team Player
• Clearly explain model behavior (e.g. thresholds, decision boundaries).
• Share experiment results, performance comparisons, and trade-offs transparently.
• Collaborate with product managers, engineers, and non-technical stakeholders.
• Align ML decisions across the team for unified communication.
• Follow lean documentation principles: be precise, but cover key code, decisions, and the chosen approach

Job Requirements

Business Segment

Connectivity

Platform

Operating Division