Support the Ecotoxicology Division’s digital initiatives by developing a digital dashboard for toxicology and ecotoxicology data related to PETRONAS products. The intern will gain hands-on experience in data management, data visualization, and digital tools used in environmental and product safety assessments.
Key responsibilities:
• Dashboard Development: Design, develop, and maintain an interactive digital dashboard to visualise toxicology and ecotoxicology data for improved decision-making and product stewardship.
• Data Management: Assist in consolidating, organising, and standardising toxicology and ecotoxicology datasets derived from internal studies, literature, and regulatory databases.
• Data Analytics & Visualisation: Apply data analytics techniques to identify trends, data gaps, and insights related to environmental and product safety. Develop clear and meaningful visualisations using tools such as Power BI or equivalent platforms.
• Process Digitalisation: Support the development of simple digital tools or workflows to streamline data review and reporting processes within the Ecotoxicology Division.
• Documentation: Prepare clear documentation for all dashboards, data workflows, and digital tools to enable knowledge retention and smooth handover.

• Currently pursuing a Bachelor’s or Master’s degree in Data Science, Computer Science, Engineering, or other related fields.
• Interest in applying digital and data-driven approaches to environmental and toxicological research.
• Familiarity with data visualisation tools such as Power BI or Tableau.
• Strong analytical and problem-solving skills with attention to detail.
• Good communication and interpersonal skills, with the ability to present information clearly.
• Ability to work independently and collaboratively in a fast-paced environment.

ADDED ADVANTAGE (Desired Skills & Experience)
• Programming skills in Python, R, or Excel VBA for data manipulation and automation.
• Basic understanding of toxicology, ecotoxicology, or environmental science concepts.
• Experience in developing dashboards or handling scientific data.
• Familiarity with data quality assessment, anomaly detection, or predictive analytics