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Tier-1 brand, common Data Scientist role, mid-level experience, metro location, and broad skillset drive high competition.
Requires specialized process-mining and event-data experience, reducing transferability across industries.
Explicit 6–8 year requirement plus process-data and MLOps/platform experience makes shortlisting highly strict.
Develop and implement machine learning and optimization models on process event data to predict, classify, and improve business process performance.
Design and maintain process data models and pipelines, integrating data from enterprise systems (e.g., SAP, Salesforce) for process mining and ML applications.
Collaborate with data scientists, engineers, and developers to embed models into Shell's process-mining platform, ensuring scalable real-time analytics and MLOps implementation.
6–8 years of experience in data science including 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.
Strong programming skills in Python and SQL, with experience in process data modeling and machine learning techniques.
Experience with process-mining platforms (e.g., Celonis EMS, PM4Py) and knowledge of MLOps and DevOps practices.
Experienced in applying machine learning, statistical modeling, and process-mining algorithms to large enterprise datasets focused on process optimization.
Ability to design and deploy scalable analytical solutions integrated into process-mining ecosystems using Agile and CI/CD methodologies.
Familiar with responsible AI principles and committed to technical rigor and model explainability in production environments.