AI/ML Data Analyst for Manufacturing Insights

Hartalega; Sepang; On-site; MYR 120,000 - 180,000; Full time

Job summary

Hartalega in Malaysia is seeking a data scientist to extract insights from operational data and drive AI initiatives across the business. This role bridges IT processes with external technology vendors, ensuring data readiness and advising on AI methodologies.

Responsibilities include cleaning data pipelines for ML readiness, implementing production ML/AI algorithms, training and deploying models, and building dashboards to monitor KPIs and model drift.

Qualifications

• Bachelor’s degree required in data science or related field.
• Minimum 4 years in data analysis or ML-focused role.
• Experience evaluating data for visualization.
• Familiarity with Grafana, Node-RED, InfluxDB, BI tools preferred.
• Certifications in Data Science/ML/Cloud are advantageous.

Responsibilities

• Assess and clean data pipelines for ML/AI readiness.
• Implement ML/AI algorithms using production data.
• Lead training and deployment of AI/ML models.
• Manage data deployments across cloud and on-premise environments.
• Design test plans to validate model accuracy and viability.
• Perform exploratory data analysis to identify trends and opportunities.
• Develop dashboards to track KPIs and model drift.
• Enforce data governance and security per Hartalega policies.
• Collaborate with IT and engineers to resolve data quality issues.
• Maintain project documentation and secure design file systems.

Skills

Data analysis

ML/AI

Dashboard design

Data governance

Stakeholder comms

Python programming

Education

Bachelor's degree in data science or related field

Tools

Grafana

Node-RED

InfluxDB

PowerBI

Tableau

Job description

Hartalega in Malaysia is seeking a data scientist to extract insights from operational data and drive AI initiatives across the business. This role bridges IT processes with external technology vendors, ensuring data readiness and advising on AI methodologies.

Responsibilities include cleaning data pipelines for ML readiness, implementing production ML/AI algorithms, training and deploying models, and building dashboards to monitor KPIs and model drift.