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Tier-1 brand, popular Data Scientist title, metro location, and mid-level seniority increase applicant competition.
Role requires domain-specific process-mining and enterprise event-data experience, limiting cross-industry transferability.
Explicit 6–8 years requirement, mandatory process-event experience, and platform/MLOps skills create strict shortlisting filters.
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Develop and deploy machine learning and optimization models using process event data to predict, classify, and improve business-process performance.
Design, implement, and maintain process data models and analytical pipelines integrating data from enterprise systems (e.g., SAP, Salesforce) for process mining and AI/ML applications.
Collaborate with engineers and data scientists to embed models into Shell’s process-mining platform and manage model lifecycle using MLOps and DevOps practices.
6–8 years of experience in data science including at least 2 years working 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, and experience with process-mining platforms such as Celonis EMS or open-source frameworks (PM4Py, ProM).
Familiarity with MLOps, DevOps, Agile development, and cloud environments (Azure, AWS, or GCP).
Experienced in applying machine learning, process mining, and optimization in large enterprise datasets to build scalable analytical solutions.
Skilled in designing reusable data schemas and pipelines for process analytics, with a commitment to Responsible and Explainable AI principles.
Able to collaborate cross-functionally in a technology-driven environment focused on process transformation and innovation.