Data Analyst

Project description

A leading provider of drilling fluid, solids control, and waste management solutions that support well construction and industrial drilling operations. Its technologies are designed to improve drilling efficiency, maintain wellbore stability, optimize fluid performance, manage drilling waste, and protect reservoir integrity. The portfolio includes water-based, synthetic-based, and nonaqueous fluid systems, as well as equipment and services for solids separation, fluid recovery, and environmental compliance. These solutions are widely applied in oil and gas, geothermal, mining, water well, horizontal directional drilling, and civil engineering projects.

Responsibilities

• Develop predictive models to optimize drilling performance, fluid properties, and operational efficiency. Analyze real-time drilling and sensor data to detect anomalies, reduce nonproductive time (NPT), and improve wellbore stability. Build machine learning solutions for forecasting drilling risks, lost circulation events, equipment failures, and fluid losses. Design data pipelines and dashboards that integrate operational, geological, and engineering data for decision support. Apply statistical analysis and advanced analytics to identify performance trends and recommend process improvements. Develop digital solutions for drilling fluids monitoring, solids control optimization, and waste management efficiency. Collaborate with drilling, fluids, and operations engineers to translate business challenges into data-driven solutions. Implement data quality, governance, and validation frameworks to ensure reliable operational analytics. Create visualization tools and KPIs for tracking drilling performance, fluid system effectiveness, and cost optimization.

SKILLS

Must have

• Strong proficiency in Python and SQL for data analysis, machine learning, and data engineering. Experience with machine learning algorithms, predictive analytics, and statistical modeling. Expertise in data visualization tools such as Power BI, Tableau, or Plotly. Experience processing large-scale time-series, sensor, and operational data. Knowledge of cloud platforms (Azure, AWS, or GCP) and big data technologies. Strong understanding of data pipelines, ETL processes, and data quality management. Experience with anomaly detection, forecasting, and predictive maintenance applications. Solid understanding of statistical analysis, hypothesis testing, and experimental design. Ability to develop and deploy machine learning models in production environments. Experience working with cross-functional engineering and operations teams. Strong problem-solving, communication, and stakeholder management skills.

Nice to have

Experience analyzing real-time drilling, completions, or industrial operational data. Knowledge of drilling fluids, solids control, wellbore stability, and drilling performance KPIs. Understanding of IoT, telemetry, SCADA, and sensor-based monitoring systems. Experience building predictive models for operational risk reduction and performance optimization. Familiarity with digital transformation initiatives in energy, manufacturing, mining, or heavy industry environments. Ability to translate engineering challenges into scalable data science solutions.