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Data Scientist

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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
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