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.
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