Job Purpose:
Responsible for extracting, evaluating, and transforming operational data into actionable insights while playing a pivotal role in Hartalega's AI and machine learning initiatives. This role will act as a bridge between internal processes and external technology vendors, ensuring data readiness and advising on the best AI methodologies.
Responsibilities:
- Assess and clean data pipelines to ensure readiness for ML/AI applications.
- Implement optimal ML/AI algorithms using production data to meet process requirements.
- Lead the training and deployment of AI/ML models in production environments.
- Manage data deployments across cloud and on-premise environments, supporting vendor-led AI initiatives.
- Design and execute rigorous test plans to validate the accuracy and commercial viability of AI models before deployment.
- Perform exploratory data analysis (EDA) to identify trends, anomalies, and optimization opportunities in manufacturing.
- Develop and optimize interactive dashboards to track KPIs, operational metrics, and AI model drift.
- Enforce data governance, documentation, and security standards per Hartalega policies.
- Partner with IT and manufacturing engineers to gather requirements and resolve data quality issues.
- Ensure all project documentation is completed accurately. Manage and secure the design drawing/file system in compliance with IT policies. Any other tasks relevant to your skillset that assigned by your superior.
Qualification & Experience:
- Bachelor's degree in data science, Computer Science, Statistics, Mathematics, Information Technology, or a closely related quantitative field.
- Minimum 4 years of relevant working experience in data analysis, data engineering, or a machine learning-focused role.
- Strong capability in evaluating data to determine the optimal visual presentation strategy.
- Hands-on knowledge and practical experience with Grafana, Node-RED, and InfluxDB will be considered a significant added advantage, alongside familiarity with other BI tools (e.g., PowerBI, Tableau).
- In-depth knowledge of ML methodologies (supervised/unsupervised learning, predictive modelling, deep learning) and the ability to recommend best-fit algorithms for industrial applications.
- Competent with high integrity and strong analytical skills;
- Result-oriented with the ability to work independently with all levels of employees.
- Demonstrate high leadership and managerial skill, especially identifying tackling and presenting issues.
- Certifications in Data Science, Machine Learning, or Cloud Infrastructure (Oracle, Huawei, AWS, Azure) are highly advantageous.
