Pipeline Infrastructure Nigeria Limited (PINL) is wholly Nigerian company incorporated in 2019 to render various services to Operators and Key Players in the Nigerian oil and gas sector.
Job Summary
- We are seeking a highly analytical and detail-oriented Data Analyst to join our team in the oil and gas sector.
- The ideal candidate will be responsible for collecting, processing, and analyzing complex datasets to support data-driven decision-making.
- This role requires strong data visualization skills, statistical analysis capabilities, and industry-specific knowledge to optimize operations and business performance.
Key Responsibilities
- Collect, clean, and analyze large datasets related to production, operations, and market trends in the oil and gas sector.
- Develop and maintain dashboards, reports, and data models to support business intelligence and strategic planning.
- Provide insights and recommendations based on data analysis to improve efficiency and reduce costs.
- Work closely with engineering, finance, and operations teams to identify data needs and drive analytical solutions.
- Utilize predictive analytics and machine learning models to forecast trends and enhance decision-making.
- Ensure data integrity, accuracy, and compliance with industry regulations.
- Present findings to management and stakeholders through clear reports and visualizations.
Qualifications & Experience
- Bachelor’s degree in Data Science, Statistics, Engineering, Computer Science, or a related field.
- Minimum of 4 years of experience in data analysis, preferably in the oil and gas industry.
- Proficiency in SQL, Python, R, or other data analysis tools.
- Strong experience with data visualization tools such as Power BI, Tableau, or similar.
- Knowledge of oil and gas industry metrics, production data, and market trends.
- Experience in working with large datasets and database management.
- Strong problem-solving skills and attention to detail.
Preferred Skills:
- Experience with geospatial data and GIS tools.
- Familiarity with industry-specific software and databases.
- Knowledge of predictive modeling and machine learning techniques.
Method of Application
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