Data Scientist, AI/ML Predictive Maintenance Platform

Keppel Ltd.; Singapore; On-site; SGD 90,000 - 140,000; Full time

Job summary

Keppel Ltd. in Singapore seeks a skilled Data Scientist to shape an 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 and analytics.

You will own experiments with rapid iterations, build monitoring dashboards, collaborate with product managers and engineers, and ensure models meet reliability, explainability, and deployment constraints for real-world use cases.

Qualifications

• Experience building ML models for time-series data.
• Ability to deploy models to production environments.
• Strong collaboration with product managers, engineers and stakeholders.

Responsibilities

• Develop, train, and fine-tune models on time-series sensor data for anomaly detection and failure prediction.
• Explore forecasting, anomaly detection, changepoint detection, and representation learning techniques.
• Iterate rapidly and deliver working ML models to production every 1–2 weeks.
• Monitor performance, manage drift, and implement retraining strategies.
• Explain model behavior and align decisions with cross-functional teams.

Skills

Time-series data handling

Anomaly detection

Failure prediction

LSTM/Transformer models

Hybrid modelling

Experiment ownership

Production ML deployment

Collaboration with stakeholders

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

1. 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

2. 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.

3. 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