Data Scientist

Role Overview

The Commercial Data Science team partners with Shell’s business units to deliver data-driven solutions through deep process understanding and technical excellence. As a Data Scientist, 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.

What You Will Do

Develop 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.

Why It Might Be a Fit

You will collaborate with senior data scientists, data engineers, and software developers to embed models into Shell’s process-mining platform for scalable, real-time analytics. Ensure Responsible and Explainable AI (XAI) principles are embedded in all model development and deployment practices.

Requirements

• 6–8 years of experience in data science, including hands-on development of AI/ML or optimization models
• 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 environments (Azure, AWS, or GCP) for data science workloads
• Familiarity with Celonis EMS, SAP Signavio, or similar process-mining platforms
• Knowledge of process simulation, optimization, or operations research methods
• Exposure to big data frameworks (Spark, Databricks) for handling large-scale event data
• Ability to actively engage in professional communities and forums to share insights and collaborate on advancements in data science and analytics
• Strong analytical thinking and technical documentation skills
• Experience with open-source process-mining frameworks (PM4Py, ProM)
• Research contributions or proof-of-concept development in process intelligence or applied AI
• Familiarity with Responsible AI frameworks (e.g., interpretability libraries, bias detection tools)

Benefits

• Opportunities for growth and development
• Collaborative and inclusive work environment
• Access to a wide range of training and development programs
• Inclusive and progressive workplace
• Closing the gender gap and promoting equality for employees regardless of sexual orientation or gender identity