Data Scientist

Full Time, onsite
Manpower Staffing Services (Malaysia) Sdn. Bhd.
Petaling Jaya, Malaysia

RM 5,000 - RM 7,000 / Per Mon

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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.
        Working Hours & Days : 8am - 5pm (Monday - Friday)
        Job Type : Permanent

Minimum 3 years experience
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.