Job Description Summary

Job Description

Roles and Responsibilities

• Own and lead assigned DS/ML workstreams as an individual contributor: collaborate with stakeholders to frame problems and agree success metrics then deliver to plan.
• Perform data acquisition quality assessment/cleansing feature engineering and exploratory analysis across industrial datasets (sensor/telemetry production logs emissions maintenance history) ensuring reproducibility.
• Develop tune and validate models (regression classification time-series such as ARIMA/Prophet/LSTM/GRU/state-space; anomaly detection; ensembles; deep learning where applicable) with robust cross-validation and clear documentation.
• Deploy and operationalize models on cloud ML platforms (AWS/Azure/GCP) under established practices; contribute to serving choices and implement monitoring drift detection and retraining per defined policies in collaboration with MLOps and platform teams.
• Build maintainable production-ready assets for assigned use cases: pipelines experiment tracking code quality and reusable components; adhere to governance security and reliability/SLAs.
• Translate model outcomes into actionable insights for technical and non-technical stakeholders; communicate trade-offs risks and assumptions; track value against success metrics.
• Provide informal mentorship (code reviews modeling best practices) to junior team members; contribute templates and documentation to improve ways of working.
• Contribute to pilots/POCs in GenAI/LLM-assisted workflows (analytics automation documentation knowledge retrieval) as an added advantage.
• Where applicable partner with Reliability Engineering to apply reliability-focused models (e.g. Weibull/survival/RGA) and integrate CMMS/EAM/APM and historian/SCADA data to inform maintenance and spares decisions.
• Stay current with advances in industrial ML (e.g. streaming/real-time) and apply incremental improvements to methods and patterns.

Education Qualification

For roles outside USA: Bachelors Degree in Computer Science or STEM Majors (Science Technology Engineering and Math) with minimum 5 to 8 years of experience in Data Science/Machine Learning or closely related roles. Masters preferred.

For roles in USA: Bachelors Degree in Computer Science or STEM Majors (Science Technology Engineering and Math) with minimum 8 years of experience. Masters preferred.

Desired Characteristics

Technical Expertise:

• Proficient in Python and SQL with libraries such as Pandas NumPy scikit-learn; experience with TensorFlow/PyTorch where deep learning is applicable.
• Strong applied time-series and anomaly detection for industrial data; hands-on with feature engineering and model validation practices.
• Experience deploying on cloud ML platforms (e.g. AWS SageMaker Azure ML GCP Vertex AI); familiarity with MLOps (CI/CD for ML model registry monitoring drift detection retraining).
• Solid data management practices: ETL fundamentals data quality assessment/cleansing and awareness of governance/security controls.
• Familiarity with big data/streaming technologies (e.g. Spark Kafka) and real-time analytics considerations is a plus.
• Preferred/added advantage: Reliability analytics methods and tools (Weibull survival/hazard modeling RGA/Crow-AMSAA; ReliaSoft suite or open-source equivalents such as lifelines/scikit-survival). GenAI/LLM-enablement for analytics acceleration.

Domain Knowledge:

• Minimum 4 years experience in operations within at least one of: Oil & Gas Fossil Power Renewable Power; ability to connect operational realities (failure modes maintenance strategies process constraints) to features validation criteria and deployment constraints.
• Demonstrated business understanding: map analytics to operational KPIs (availability MTBF/MTTR throughput energy yield emissions cost) and articulate value/ROI trade-offs.

Leadership:

• Operates with some autonomy within standard practices; primarily an individual contributor with strong interpersonal skills; provides informal guidance to new team members.
• Structured problem solving with the ability to propose options beyond set parameters (with guidance); collaborates across functions to execute effectively.
• Consulting mindset: translates requirements and trade-offs for stakeholders; provides researched recommendations with documented assumptions.
• Acts as a change agent at team level: adopts new methods/tools and drives continuous improvement in ways of working.

Personal Attributes:

• Curiosity and creativity: explores new approaches and connects ideas from adjacent domains to improve outcomes.
• Comfort in ambiguity: delivers with assumptions where needed and course-corrects based on feedback; communicates status and limitations clearly.
• Strong communication and collaboration skills: tailors messages to varied audiences and contributes to a positive high-performance team culture.

Note: To comply with US immigration and other legal requirements it is necessary to specify the minimum number of years experience required for any role based within the USA. For roles outside of the USA to ensure compliance with applicable legislation the JDs should focus on the substantive level of experience required for the role and a minimum number of years should NOT be used.

This Job Description is intended to provide a high level guide to the role. However it is not intended to amend or otherwise restrict/expand the duties required from each individual employee as set out in their respective employment contract and/or as otherwise agreed between an employee and their manager.

Additional Information