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1. TTE (Time-To-Event) Modelling Expertise:
- Experience with survival analysis and TTE modelling techniques.
- Proficiency in survival analysis and hazard function modelling.
- Understanding of Kaplan-Meier estimators, Cox proportional hazard models, Bayesian
hierarchical models, Harell’s C-index, parametric survival models, etc.
- Experience handling censored data and time-dependent covariates.
- Familiarity with libraries such as:
i. lifelines: A Python library for survival analysis.
ii. Scikit-survival: Built on scikit-learn, focuses on survival modelling.
iii. PySurvival: For predictive survival modelling.
2. Machine Learning and AI Development:
- Strong knowledge of machine learning frameworks (e.g., TensorFlow, PyTorch, Scikit learn).
- Solid understanding of supervised and unsupervised learning.
- Experience with libraries like scikit-learn, TensorFlow, or PyTorch.
- Experience in designing, training, and maintaining machine learning models for predictive
maintenance.
- Familiarity with explainable AI (XAI) techniques for better model interpretability.
3. Deep Learning
- Familiarity with deep learning concepts and frameworks (Keras, TensorFlow, or PyTorch).
- Knowledge of neural networks, RNNs, and architectures suitable for time-series data.
4. Programming Skills:
- Proficiency in Python, with strong fundamentals in object-oriented programming and
functional programming.
- Familiarity with Python libraries like NumPy, pandas, and SciPy for data manipulation and
scientific computation.
- Expertise in Python and/or R for data analysis, modelling, and system integration.
- Knowledge of SQL and NoSQL databases for handling large datasets.
- Familiarity with scripting and automation for data preprocessing and system updates.
5. Data Engineering and Management:
- Handling missing data, encoding categorical features, and time-to-event specific
preprocessing.
- Experience with transforming time-dependent covariates.
6. Data Preprocessing and Feature Engineering
- Proficiency in managing and processing IoT data streams.
- Experience with big data technologies
- Understanding of cloud platforms (e.g., AWS (preferably), Azure, Google Cloud) for
deploying and scaling AI solutions.
7. Data Visualization:
- Skills in creating visualizations for model interpretation and evaluation.
- Tools: Matplotlib, Seaborn, Plotly, and survival curves visualization in lifelines.
8. Statistical Analysis:
- Advanced understanding of statistical methods for TTE modelling and predictive
analytics.
- Knowledge of anomaly detection techniques relevant to predictive maintenance.
- Proficient in statistical methods, hypothesis testing, and distributions.
- Knowledge of censored data handling and hazard functions.
- Tools: statsmodels, lifelines.
Job Requirements:
- Certifications in machine learning or AI (e.g., Google Professional Machine Learning Engineer,
AWS Certified Machine Learning Specialist), advanced analytics (e.g., SAS Certified Specialist:
AI and Machine Learning), and training in cloud-based AI/IoT platforms are highly valued.
- Proven track of record in implementing AI systems for similar projects, particularly in asset
management or equipment reliability
- Strong in problem solving, collaboration and easy to adapt with new tools, technologies as the
AI system evolves.
- Experience with predictive maintenance systems and IoT applications in healthcare or industrial
settings, including implementing AI systems for asset management, taking over and
maintaining AI systems post-consultant handover, and updating and retraining models to
ensure accuracy and relevance.
4. Salary package:
- Basic Salary: Range RM5,000.00 – RM7,000.00
- Handphone Allowance: RM30.00
5. Working days: 5 days a week
6. Contract duration: Permanent
7. Expected start date: As soon as possibl
Job Types: Full-time, Permanent
Pay: RM5,000.00 - RM7,000.00 per month
Benefits:
- Health insurance
- Professional development
Schedule:
- Day shift