Develop, and implement machine learning and optimization models using process event data to predict, classify, and enhance business-process performance.
Apply process-mining and AI/ML techniques to discover process patterns, identify root causes, and quantify improvement opportunities.
Implement and maintain process data models - integrating and transforming data from enterprise systems (e.g., SAP, Salesforce, ServiceNow) into structured, analysis-ready pipelines for process-mining and machine-learning applications.
Collaborate with senior data scientists, data engineers, and software developers to embed models into Shell's process-mining platform for scalable, real-time analytics.
Develop algorithms that combine process-mining insights with AI/ML models for anomaly detection, forecasting, and prescriptive optimization.
Implement MLOps and DevOps workflows for model lifecycle management, version control, deployment, and monitoring using GitHub and CI/CD pipelines.
Support the establishment of best practices in solution design, data quality, and delivery of scalable analytical products aligned with strategic business objectives.
Contribute to research and innovation by evaluating emerging tools, algorithms, and open-source frameworks in process intelligence and digital twins.
Ensure Responsible and Explainable AI (XAI) principles are embedded in all model development and deployment practices.
What you bring
6-8 years of experience in data science, including hands-on development of AI/ML or optimization models and 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 a related quantitative field.
Proven track record of building and deploying analytical solutions from concept to production.
Demonstrated expertise in applying machine learning, statistical modeling, and process-mining algorithms to large, multi-source enterprise datasets.
Strong programming proficiency in Python, SQL, and data-modeling techniques for event data.
Experience in process data model design, development, and deployment, including creation of reusable data schemas and pipelines for process analytics.
Experience integrating ML models with process-mining platforms such as Celonis EMS or open-source frameworks (PM4Py, ProM).
Familiarity with Agile development, MLOps, and DevOps practices for automation, testing, and deployment.
Solid understanding of process data structures (case IDs, activities, timestamps, attributes) and their application in process-performance modeling.
Exposure to cloud environments (Azure, AWS, or GCP) for data science workloads is desirable.
Demonstrated commitment to technical rigor and model explainability through Responsible AI practices.
Proficiency in Python, SQL, and data engineering for analytical / process data modeling.
Experience in machine learning, deep learning, or optimization algorithms applied to real-world business processes.
Strong foundation in process-mining techniques, including event-log discovery, conformance checking, and process enhancement.
Understanding of MLOps concepts (CI/CD, GitHub, Docker, MLflow) for scalable deployment.
Ability to design, test, and optimize data-driven models to enhance process visibility and performance.
Experience working in cloud
Additional Information
, India
Job Family Group:
Research and Development
Worker Type:
Regular
Posting Start Date:
June 5, 2026
Business Unit:
Finance
Experience Level:
Experienced Professionals
Job Description:
What's the role
The Commercial Data Science (CDS) team partners with Shell's business units to deliver data-driven solutions through deep process understanding and technical excellence.
As a Data Scientist within the Digital Process Twin (DPT) portfolio, you will apply process mining, optimization, and AI/ML techniques to process-centric data to identify inefficiencies, predict process behavior, and optimize end-to-end business performance. You will collaborate closely with engineers, architects, and fellow data scientists to design scalable analytical solutions and integrate them into Shell's process-mining ecosystem.
The Digital Process Twin Centre of Excellence (CoE) aims to "Empower Process Transformation through Digital Process Twins" by advancing process-mining and analytics capabilities. Shell is recognized as an industry thought leader in process intelligence, pioneering innovative analytical methodologies and data-driven process transformation. The CoE collaborates with academic institutions, industry forums, and technology vendors to drive innovation and applied research in process analytics.
If you are passionate about building advanced analytical models using process event data and developing intelligent solutions that enhance operational agility and enable autonomous processes, we invite you to join us as a Data Scientist contributing to Shell's digital process transformation journey.
Shell · Chennai - Rmz Millenia