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Tier-1 brand, mid-level Data Scientist title, and metro location drive high competition despite niche process-mining skills.
Process-mining and Celonis experience add domain specificity while ML, MLOps, and data engineering skills remain moderately transferable.
Explicit 6–8 years plus mandatory process-mining, ML, MLOps, and Celonis/platform experience enforces high shortlisting strictness.
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Develop and implement machine learning and optimization models using process event data to improve business process performance.
Design, maintain, and embed scalable process data models and AI/ML algorithms into Shell’s process-mining platforms for real-time analytics.
Drive innovation by evaluating emerging tools and ensuring Responsible and Explainable AI principles in model development and deployment.
6–8 years of experience in data science with at least 2 years working on process event or behavioral data.
Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, Mathematics, or related quantitative field.
Strong programming skills in Python and SQL, and experience in process data modeling and machine learning deployment.
Experience with process-mining platforms (e.g., Celonis EMS, PM4Py, ProM) and familiarity with MLOps/DevOps practices (CI/CD, GitHub) for scalable deployment.
Experienced in applying process-mining, AI/ML, and optimization techniques specifically to large, multi-source enterprise datasets related to operational processes.
Capable of collaborating cross-functionally with engineers and data scientists to build production-level analytics embedded in business platforms.
Skilled in combining domain knowledge of process data structures with advanced technical practices in cloud environments and responsible AI.