Data Scientist jobs in Malaysia

The Data Scientist play a key role in driving digital transformation across our exploration and production operations. Support the InfoTech & Digitalization department and work closely with other business units to enhance data management practices, improve data accessibility, and develop digital solutions that boost productivity and reduce operational costs.

• Collaborate within the InfoTech & Digitalization department to improve data management practices, including data quality, governance, and lifecycle management.
• Analyze storage utilization and propose optimization strategies to reduce costs and improve performance.
• Develop solutions to enhance data accessibility and usability for end users across departments.
• Support the company’s digital transformation roadmap by identifying opportunities for automation, predictive analytics, and AI-driven insights.
• Evaluate and implement digital tools and platforms that align with business goals.
• Partner with departments such as Operations, Finance, and Engineering to identify pain points and co-develop digital products or dashboards that improve efficiency and reduce costs.
• Translate business needs into technical solutions using data science and analytics.
• Conduct training sessions and workshops to upskill staff in data literacy, analytics tools, and digital technologies.
• Promote a data-driven culture across the organization.

• Bachelor’s or Master’s degree in Data Science, Computer Science, Engineering, or related field from a recognized institution.

• More than 3 years of hands‑on experience in data science or analytics role, preferably in a complex, asset‑heavy industry such as oil & gas, energy, or manufacturing.
• Proven track record of delivering data‑driven solutions that improved operational efficiency, reduced costs, or enhanced decision‑making.
• Experience working in cross‑functional teams, collaborating with business units such as Operations, Finance, and Engineering.
• Demonstrated ability to translate business problems into analytical solutions and communicate insights to non‑technical stakeholders.
• Familiarity with managing and working with large, diverse datasets from multiple sources.
• Experience in mentoring or training colleagues in data literacy or analytics tools.
• Ability to manage multiple projects simultaneously and prioritize tasks effectively.
• Comfortable working in both structured and agile environments, adapting to evolving business needs.

• Python programming for data analysis, modeling, and automation.
• SQL programming for querying and manipulating relational databases.
• Power BI for building interactive dashboards and reports.
• Machine learning techniques such as regression, classification, and clustering.
• Data wrangling and manipulation using libraries like pandas and numpy.
• Building ETL processes and data pipelines.
• Deploying models or analytics solutions into production environments.
• Knowledge of data governance, including data quality, metadata, and lifecycle management.