Job Description Summary

Chargé de programmer un composant, une fonctionnalité et/ou un ensemble de fonctionnalités. Travaille de manière indépendante et contribue à l'équipe concernée et à d'autres équipes de l'entreprise. Vous contribuerez également aux discussions sur la conception.

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: Bachelor's 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. Master's preferred.

For roles in USA: Bachelor's Degree in Computer Science or "STEM" Majors (Science, Technology, Engineering and Math) with minimum 8 years of experience. Master's preferred.

Desired Characteristics
Technical Expertise:

• Proficient in Python and SQL with libraries such as Pandas, Num Py, scikit-learn; experience with Tensor Flow/Py Torch 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 Sage Maker, 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; Relia Soft 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:

Relocation Assistance Provided: Yes