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Tier-1 employer, general Data Scientist title and metro location increase competition, but niche process-mining skill reduces density.
Process-mining and Celonis expertise make cross-industry transfers moderately constrained despite general data science skills.
Explicit 6–8 years, mandatory process-event experience and specific process-mining and MLOps skills increase filter strictness.
Develop and implement machine learning and optimization models using process event data to improve business process performance.
Design and embed scalable analytical solutions into Shell’s process-mining platform for real-time insights and operational optimization.
Contribute to research and innovation in process intelligence, ensuring Responsible AI principles in all model development and deployment.
6–8 years of data science experience including AI/ML or optimization models development; at least 2 years with process event or behavioral data.
Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, Mathematics, or related quantitative field.
Proficiency in Python, SQL, process-mining algorithms, and data-modeling techniques for event data.
Experience with MLOps/DevOps practices, process-mining platforms (e.g., Celonis EMS), and cloud environments (Azure, AWS, or GCP).
Experienced in applying process-mining combined with AI/ML to enterprise datasets for operational improvements.
Skilled in developing scalable, production-grade analytical models integrating ML with process data pipelines.
Familiar with responsible and explainable AI frameworks and advanced deployment automation (CI/CD, GitHub, Docker).