Senior Data Scientist – Industrial Machine Learning

About the role

The Senior Data Scientist will design, develop and deploy advanced machine‑learning solutions that improve reliability, enable predictive maintenance and optimise production in complex Oil & Gas environments. You will work closely with engineers, subject‑matter experts and business stakeholders to turn operational data into actionable intelligence.

Key responsibilities

• Design, develop and deploy ML algorithms for reliability monitoring, predictive maintenance and production optimisation.
• Build supervised and unsupervised models (regression, classification) applying strong mathematical principles.
• Implement scalable solutions using distributed frameworks such as MapReduce and streaming technologies.
• Collaborate with data engineers and domain experts to identify, validate and interpret new data sources.
• Lead end‑to‑end data‑science projects from problem definition through deployment and ongoing monitoring.
• Develop rapid prototypes in Python, R or JavaScript and support Java/Scala when needed.
• Establish MLOps practices, including CI/CD pipelines, model versioning, containerisation, automated testing and performance‑drift management.
• Communicate technical insights clearly to both technical and non‑technical audiences.

Required profile

• Minimum 10 years of hands‑on experience designing, developing, deploying and operating ML solutions for industrial use cases.
• Proven experience in Oil & Gas or Manufacturing environments with deep domain knowledge of asset performance and process optimisation.
• Track record of delivering production‑grade models that generate measurable business value.

Required skills

• Python, R, JavaScript (plus Java or Scala as a plus)
• Machine‑learning algorithms, regression, classification, unsupervised learning
• Linear algebra, calculus, probability, statistics
• Distributed computing frameworks (MapReduce, streaming platforms)
• MLOps tools, CI/CD pipelines, containerisation (Docker/Kubernetes)
• Model versioning, automated testing, monitoring, performance‑drift management