Data Scientist, AI/ML Predictive Maintenance Platform
Summary
Build and deploy AI/ML models for real-time IoT predictive maintenance, using time-series techniques and Gen AI to detect anomalies and forecast failures across data-center assets.
Data Scientist – Predictive Maintenance Platform
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 anomaly detection and failure prediction across various asset types. Explore and apply 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 change‑point 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. Company Overview
Keppel is a global asset manager and operator headquartered in Singapore. With operations in more than 20 countries worldwide, we provide innovative solutions that address some of the world’s most pressing needs across the energy transition, rapid urbanisation, and increasing digitalisation. With sustainability at the core of our strategy, Keppel harnesses the strengths and expertise of our business units to develop, operate and maintain real assets, which provide diverse solutions that are good for the planet, for people and for the Company. Keppel actively seeks out bright and dynamic individuals to join our talent pool. We offer our employees opportunities to grow and shape their careers across the geographies in which we operate. Be part of our Keppel family today. Find out how Keppel is committed to shaping a brighter, better tomorrow, and building a sustainable future for all.
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 anomaly detection and failure prediction across various asset types. Explore and apply 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 change‑point 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. Company Overview
Keppel is a global asset manager and operator headquartered in Singapore. With operations in more than 20 countries worldwide, we provide innovative solutions that address some of the world’s most pressing needs across the energy transition, rapid urbanisation, and increasing digitalisation. With sustainability at the core of our strategy, Keppel harnesses the strengths and expertise of our business units to develop, operate and maintain real assets, which provide diverse solutions that are good for the planet, for people and for the Company. Keppel actively seeks out bright and dynamic individuals to join our talent pool. We offer our employees opportunities to grow and shape their careers across the geographies in which we operate. Be part of our Keppel family today. Find out how Keppel is committed to shaping a brighter, better tomorrow, and building a sustainable future for all.