Job Description Summary

Responsable de la conception, du développement, de la fourniture et de la maintenance des applications et des services logiciels. Travaille dans les domaines de la machine, du cloud, de la plate-forme et/ou des applications. Responsable du cycle de vie complet du logiciel, notamment des activités telles que l'analyse des besoins, la documentation/les procédures et la mise en œuvre.

Job Description

Roles and Responsibilities

• Execute DS/ML tasks across the model lifecycle: data acquisition, quality assessment/cleansing, feature engineering, and exploratory data analysis on industrial datasets (sensor/telemetry, logs, emissions, maintenance) with reproducible workflows.
• Train, tune, and validate models (regression, classification, and time-series methods such as ARIMA/Prophet; anomaly detection; ensembles). Document experiments and results clearly.
• Collaborate with data/platform engineering to contribute to data pipelines and model serving; support deployment activities and basic monitoring/drift checks under supervision.
• Produce clean, well-structured Python/SQL code; follow coding standards, version control, and experiment tracking practices.
• Create visual analyses and concise summaries to communicate findings and recommendations to teammates and stakeholders; incorporate feedback to iterate.
• Assist in measuring outcomes against success metrics (e.g., reliability, availability, efficiency, emissions, cost) and maintain project artifacts (reports, annotated code).
• Learn industrial context, data sources, and domain constraints; proactively identify data quality issues and propose fixes within established procedures.
• Participate in POCs/pilots, including GenAI/LLM-assisted analytics workflows (analytics automation, documentation) as an added advantage.

Education Qualification: Bachelor's Degree in Computer Science or “STEM” Majors (Science, Technology, Engineering and Math) with minimum 3 years of experience in Data Science/Machine Learning or closely related roles.

Desired Characteristics

Technical Expertise:

• Proficient in Python and SQL; hands-on with Pandas, NumPy, scikit-learn; basic exposure to TensorFlow/PyTorch is a plus.
• Applied experience with feature engineering, model selection, cross-validation, and performance measurement for time-series and classification/regression problems.
• Solid foundations in data management: ETL basics, data quality checks/cleansing, and working with large datasets.
• Familiarity with cloud ML platforms (e.g., AWS SageMaker, Azure ML, GCP Vertex AI) and MLOps concepts (experiment tracking, model registry, monitoring) is a plus.
• Competent in visual analytics for EDA and communicating insights; experience with dashboards or notebooks preferred.
• Familiarity with big data/streaming technologies (e.g., Spark, Kafka) and real-time analytics considerations is a plus.

Domain Knowledge:

• Exposure to industrial operations (Oil & Gas, Fossil Power, Renewable Power) is a plus; ability to learn failure modes, maintenance strategies, and process constraints and translate them into features and validation criteria.
• Basic understanding of business drivers and operational KPIs (availability, MTBF/MTTR, throughput, energy yield, emissions, cost) with the ability to connect analytical results to business value.

Personal Attributes:

• Curiosity and continuous learning mindset; connects ideas and incorporates feedback quickly.
• Comfort in ambiguity at project/task level; states assumptions clearly and adapts based on new information.
• Clear communicator; explains technical information to teammates and stakeholders and asks clarifying questions to ensure shared understanding.

Additional Information