Data Scientist - Digital Process Twin

Role Overview Shell's Commercial Data Science team is looking for a skilled Data Scientist to join the Digital Process Twin portfolio in Chennai. In this role, you will harness the power of process mining, AI/ML, and optimization techniques to transform process event data into actionable intelligence. You will be part of a Centre of Excellence that is recognized globally as a thought leader in process intelligence and digital process transformation. Key Responsibilities - Build and deploy machine learning and optimization models using process event data to predict, classify, and improve business process performance - Apply process mining and AI/ML methods to uncover process patterns, identify root causes, and quantify areas for improvement - Design and maintain process data models by integrating data from enterprise platforms such as SAP, Salesforce, and ServiceNow into structured pipelines ready for analysis - Work alongside senior data scientists, data engineers, and software developers to embed analytical models into Shell's process mining platform for real-time and scalable use - Develop algorithms that combine process mining insights with AI/ML capabilities for anomaly detection, forecasting, and prescriptive optimization - Implement MLOps and DevOps workflows including version control, CI/CD pipelines, and model monitoring using GitHub and related tools - Evaluate emerging tools, open-source frameworks, and algorithms in the fields of process intelligence and digital twins - Ensure all model development follows Responsible AI and Explainable AI principles throughout the entire lifecycle Required Qualifications - Bachelor's or Master's degree in Computer Science, Data Science, Engineering, Mathematics, or a related quantitative discipline - 6 to 8 years of hands-on experience in data science with at least 2 years working directly with process event or behavioral data - Strong programming skills in Python and SQL along with expertise in data modeling techniques for event-driven data - Demonstrated experience integrating ML models with process mining platforms such as Celonis EMS or open-source tools like PM4Py or ProM - Solid understanding of process data structures including case IDs, activities, timestamps, and attributes - Familiarity with MLOps concepts including CI/CD, Docker, MLflow, and GitHub - Exposure to cloud environments such as Azure, AWS, or GCP for data science workloads - Knowledge of big data frameworks like Spark or Databricks is an advantage - Experience with Responsible AI frameworks including interpretability libraries and bias detection tools Why Join Us At Shell, you will work alongside some of the most talented professionals in the energy and technology space, contributing to one of the world's most important energy transition journeys. Shell offers a diverse, inclusive, and values-led culture where your growth, well-being, and career development are genuinely prioritized.

- 6 to 8 years of experience in data science with AI/ML or optimization model development - At least 2 years working with process event or behavioral data - Proficiency in Python and SQL for analytical and process data modeling - Experience with process mining platforms such as Celonis EMS, SAP Signavio, PM4Py, or ProM - Strong understanding of MLOps and DevOps practices including CI/CD and GitHub - Familiarity with cloud platforms such as Azure, AWS, or GCP - Knowledge of process mining techniques including event log discovery and conformance checking - Experience with big data frameworks like Spark or Databricks - Understanding of Responsible AI and Explainable AI principles - Bachelor's or Master's degree in Computer Science, Data Science, Engineering, or Mathematics

- Opportunity to work on global energy transition projects - Access to extensive training and development programmes - Exposure to cutting-edge technology and industry-leading practices - Collaborative work environment with internationally experienced colleagues - Inclusive and diverse workplace culture - Career growth opportunities across national and international teams - Support for employees with disabilities through workplace adjustments - Engagement with academic institutions and industry innovation forums