JOB DESCRIPTION
We are looking for a Data Scientist to build and operationalize machine learning solutions for failure prediction and early-warning detection across data center and industrial assets.
The role focuses on identifying degradation patterns, forecasting asset health, detecting anomalies, and estimating time to failure from sensor data. Success requires strong experimentation skills, the ability to work with limited failure examples, and a disciplined approach to validating models in real-world environments.
Candidates should demonstrate both technical depth and a strong research mindset.
Key Responsibilities
1. 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.
2. 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.
3. 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.
4. 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.
5. 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.
JOB REQUIREMENTS
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.
BUSINESS SEGMENT
Connectivity
PLATFORM
Operating Division
