Data Scientist

Hibiscus Petroleum Berhad; Kuala Lumpur; On-site; MYR 80,000 - 100,000; Full time

Job summary

A leading energy company in Kuala Lumpur seeks a Data Scientist to drive digital transformation by enhancing data management and developing solutions that boost productivity and reduce costs. The ideal candidate will have over 3 years of experience in data science, strong technical skills in Python and SQL, and the ability to collaborate across departments. This role offers the opportunity to impact decision-making and operational efficiency within the organization.

Qualifications

• More than 3 years of experience in data science or analytics role.
• Experience in cross-functional teams and collaborating with business units.
• Ability to manage multiple projects simultaneously.

Responsibilities

• Collaborate to improve data management practices.
• Analyze storage utilization and propose optimization strategies.
• Support digital transformation by identifying automation opportunities.

Skills

Python programming

SQL programming

Power BI

Machine learning techniques

Data wrangling with pandas and numpy

Building ETL processes

Education

Bachelor’s or Master’s degree in Data Science, Computer Science, Engineering

Job description

The Data Scientist plays 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.

KEY ACCOUNTABILITIES

• 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.

REQUIREMENTS AND QUALIFICATIONS

Education:

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

Working experience:

• 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.

Technical skills:

• 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.