Title:

ESP Data ScientistESP Data ScientistEDAT35 - Principal Technical Professional - Data ScienceKBR Sustainable Technology Solutions (STS) provides holistic and value-added solutions across the entire asset life cycle. These include world-class licensed process technologies, differentiated advisory services, deep technical domain expertise, energy transition solutions, high-end design capabilities, and smart solutions to optimize planned and operating assets.ESP Data Scientist play a key role in turning data into practical solutions that keep Electric Submersible Pumps (ESPs) running reliably and efficiently. I use analytics and visualization tools to uncover trends, solve problems, and support better decisions that improve production and reduce downtime.I work closely with the ESP Technology Team Lead and other stakeholders to make sure analytics efforts meet real operational needs. This includes creating and maintaining dashboards, building predictive models when they add value, and automating routine tasks to make data easier to access and use.I also help the team build confidence in data by improving quality, promoting good practices, and sharing knowledge on tools like Power BI, Python, and SQL. While the ESP Technology Team Lead sets the technical direction, I make those strategies real through clear, reliable analytics and reporting.ResponsibilitiesLeadership

Promote practical data use: Encourage the team to use data and analytics in simple, effective ways that improve ESP performance and support business goals.

Coach and upskill: Help colleagues build skills in Power BI, Python, and SQL. Share tips, examples, and good practices.

Advise and translate: Make complex data easy to understand. Turn findings into clear recommendations that fit technical and operational plans.

Work across teams: Keep communication open with ESP engineers, IT, reliability, and production. Model integrity, accountability, and inclusive behavior.

Solve problems: Tackle data issues, improve data quality, and design straightforward analytics that save time and reduce errors.

Check the work: Validate key results and follow data governance standards.

Support change: Help the team adopt useful analytics and, where relevant, machine learning only when it adds real value.

Risk / Operating Discipline

Protect data integrity: Keep data accurate, validated, and secure across all analytics and reports. Run quality checks and support regular audits.

Monitor performance: Build and maintain dashboards that give real-time visibility into ESP performance so issues can be spotted early.

Analyze risks: Use analytics—and machine learning when it adds value—to identify trends, vulnerabilities, and potential risks. Share insights that help the team plan and prevent problems.

Support investigations: Provide clear, data-driven input for root cause analysis and incident reviews.

People

Share knowledge: Act as a subject matter expert in data analytics. Help the ESP Squad adopt data-driven practices and make the most of tools like Power BI, Python, and SQL.

Collaborate and support: Work closely with the ESP Technology Team Lead and other stakeholders to ensure analytics efforts align with technical strategies and operational goals.

Engage the team: Encourage innovation and proactive problem-solving by promoting a culture of experimentation and learning.

Step in when needed: Help resolve data challenges, close skill gaps, and motivate colleagues to achieve high standards of performance and creativity.

Production Delivery, Optimisation & Excellent Performance

Use data to improve performance: Analyze ESP data to find trends, root causes, and opportunities for optimization. Turn insights into practical actions that support production and reliability goals.

Apply continuous improvement: Use simple improvement cycles (e.g., PDCA) to plan, test, and refine analytics solutions for maximum impact.

Build clear dashboards: Design and maintain interactive dashboards (e.g., Power BI) that give real-time visibility into ESP performance and support proactive decisions.

Investigate issues: Support investigations into production anomalies using statistical analysis and, when useful, machine learning to uncover patterns and recommend fixes.

Work as a team: Coordinate with the ESP Technology Team Lead, operations, and engineering to align analytics work with business priorities and resources.

Stay ahead of risks: Monitor data for early warning signs of equipment failure or inefficiency and suggest preventive actions to reduce downtime.

Embed insights in workflows: Make sure analytics-driven insights are part of daily operations and planning processes.

Keep improving: Continuously refine data models, reports, and analytics processes to make them more accurate, useful, and aligned with changing business needs.

Experience, Qualifications & Key Skills

Industry Experience

• 5–8 years in data science, data engineering, or advanced analytics, including at least 3 years in oil & gas or industrial operations.

  
  

• Experience with ESP systems or production optimization is a strong plus.

  
  

Education

• Bachelor’s degree in computer science, Engineering, Mathematics, or a related technical field.

  
  

• Certifications in Data Science, Machine Learning, or AI are an advantage.

  
  

Technical Expertise

• Strong skills in Python, SQL, and Power BI for analytics, automation, and visualization.

  
  

• Experience with database systems (e.g., Microsoft SQL Server, Oracle) and platforms like FMD, OFM, and PI-OSI.

  
  

• Ability to build and deploy machine learning models (e.g., regression, ensemble methods, boosting) and integrate them into workflows via APIs.

  
  

• Familiarity with cloud environments.

  
  

Analytical & Statistical Skills

• Solid understanding of statistical modeling, feature engineering, and performance analytics.

  
  

• Comfortable working with large, complex datasets to produce actionable insights.

  
  

Industry Knowledge

• Good understanding of ESP operations, production optimization, and performance management systems.

  
  

• Awareness of upstream oilfield operations and lifecycle management.

  
  

Core (Non-Technical) Skills

• Strong communication and storytelling skills to turn technical insights into business value.

  
  

• Ability to coach and mentor others in analytics practices.

  
  

• Organized, adaptable, and good at managing time.

  
  

• Collaborative mindset with the ability to influence across teams.

  
  

• Commitment to continuous learning and innovation.

  
  

Work Pattern - Rotation 14/14

Iraqi nationals are preferred.

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