Data Scientist (AI/ML)

VECTOR SEARCH PROFESSIONALS; Singapore; On-site; SGD 100,000 - 140,000; Full time

Job summary

VECTOR SEARCH PROFESSIONALS in Singapore seeks a data scientist to develop predictive maintenance models using operational sensor data for failure prediction, asset degradation monitoring, remaining useful life estimation, and time-to-threshold forecasting.

You will apply survival analysis, time-series forecasting, anomaly detection, change-point detection, and representation learning, selecting approaches based on objective metrics and documented experiments, while balancing performance with

Qualifications

• Requires strong foundation in ML concepts and evaluation techniques.
• Experience with time-series data and sensor data preferred.
• Familiarity with anomaly detection, change-point detection, and representation learning.
• Ability to translate operational problems into ML solutions and document experiments.

Responsibilities

• Develop and validate models using sensor data for failure prediction and RUL estimation.
• Apply survival analysis, time-series forecasting, anomaly detection, and representation learning.
• Balance performance, explainability, and production deployment requirements.
• Collaborate with product and engineering teams, domain experts, and data center professionals.

Skills

Machine Learning

Time-Series

Anomaly Detection

Predictive Maintenance

Data Analysis

Education

Bachelor's or Master's in Engineering/CS/DS

Job description

Responsibilities

Predictive Maintenance & Early-Warning Model Development

• Develop and validate models using operational sensor data for failure prediction, asset(mechanical and electrical equipment) degradation monitoring, early-warning detection, remaining useful life estimation, and time-to-threshold forecasting.
• Apply and compare survival analysis, time-series forecasting, anomaly detection, change-point detection, and representation learning techniques.
• Select modelling approaches using measurable performance criteria and documented experimental results.
• Balance detection performance, false-positive rates, explainability, and production deployment requirements.

Working with Sparse Failure Data

• Develop strategies for environments where no failure events.
• Work with limited labelled failures, class imbalance, weak supervision, proxy labels, transfer learning, and failure-data sourcing.
• Establish validation methods that accurately measure model effectiveness despite limited failure examples.

Experimentation & Research

• Form hypotheses and design experiments to evaluate competing approaches.
• Define baseline approaches, evaluate alternatives, measure improvements using objective metrics, and document findings and limitations.
• Justify model selection using evidence rather than preference.

Model Productionization & Monitoring

• Deploy models into development and production environments.
• Monitor precision, recall, drift, and data quality.
• Establish retraining, rollback, and retirement criteria for production models.
• Investigate performance degradation and implement corrective actions.

Cross-Functional Collaboration

• Collaborate with product managers, software engineers, platform engineers, and subject matter experts (data center domain with mechanical and electrical engineering expertise)
• Translate operational problems into machine learning problems.
• Incorporate domain expertise into model development, validation, and alert interpretation.

Requirements

• Bachelor's or Master's degree in Engineering, Computer Science, Data Science, or a related field; specialization in AI/ML is preferred. Mechanical engineering background or strong exposure to mechanical/industrial systems will be an advantage.
• 4-7 years of relevant experience in data science, or applied machine learning, either in a large software development organization or an end-user engineering/industrial environment.
• Minimum 2-3 years of hands-on experience developing failure prediction, anomaly detection, condition monitoring, or predictive maintenance models using engineering, operational, or IoT sensor data.
• Good understanding of common machine learning approaches and the intuition behind them, including time-series models, anomaly detection methods, classification/regression models, and model evaluation techniques.