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Tier-1 brand, metro location, and broad skillset make applicant competition high.
Process-mining, enterprise systems, and specialized event-data skills create strong domain-specific background requirements.
Explicit 6–8 years, required process-mining and ML expertise enforce strict candidate filtering.
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Develop and deploy machine learning and optimization models using process event data for process performance prediction and enhancement.
Apply process-mining and AI/ML techniques to identify inefficiencies, root causes, and optimize end-to-end business processes.
Collaborate with engineers and data scientists to integrate scalable analytical solutions into Shell’s process-mining platform and maintain data pipelines for real-time analytics.
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 data modeling techniques for event data; experience with process mining platforms such as Celonis EMS or open-source frameworks like PM4Py, ProM.
Familiarity with Agile, MLOps, DevOps practices, and CI/CD pipelines; experience deploying models in cloud environments (Azure, AWS, or GCP) is desirable.
Experienced in building scalable AI/ML and process-mining analytical solutions for enterprise business processes with a strong technical rigor and commitment to Responsible AI principles.
Skilled in process data model design and implementation, with familiarity in embedding models within process-mining platforms and handling large multi-source datasets.
Comfortable working collaboratively in multidisciplinary teams involving data scientists, engineers, and software developers, and active in professional data science and analytics communities.