What s the role

The Commercial Data Science (CDS) team is a community of experts partnering with Shell s business stakeholders to deliver data-driven solutions through deep process understanding and technical excellence

As a Data Science Engagement Lead within the Digital Process Twin (DPT) portfolio, you will lead data scientists and collaborate with cross-functional business teams to identify transformation opportunities using process-centric data and guide the application of process mining, optimization, and AI/ML techniques to improve operational performance

Your work will drive measurable business outcomes by enhancing visibility, agility, and efficiency across Shell s business operations

The DPT Centre of Excellence (CoE) aims to Empower Process Transformation through Digital Process Twins by applying advanced process mining and analytics capabilities

Shell is recognized as an industry leader in process mining, pioneering innovative analytical approaches and advancing the process twin concept

The CoE actively engages with academic partners, industry forums, and technology vendors to drive research, innovation, and thought leadership

If you are passionate about applying data science to process event data to drive organizational process transformation and value collaboration, innovation, and continuous learning we invite you to join us as a Data Science Engagement Lead

What you ll be doing

Lead process-mining consulting engagements, translating business challenges into analytical problem statements and actionable insights that drive measurable digital process transformation
Partner with global and regional business stakeholders to uncover inefficiencies, define transformation opportunities, and generate process-analytics use cases aligned with the Integrated Process Plan (IPP)
Oversee the design and development of process data models, collaborating with process and data architects to ensure clean, integrated, and analysis-ready event data from enterprise systems (eg, SAP, Salesforce, ServiceNow)
Facilitate process discovery and value workshops, translating analytical findings into tangible business outcomes through clear, evidence-based, and persuasive data storytelling
Guide the delivery of analytical and AI/ML solutions, ensuring alignment with strategic priorities, technical excellence, and measurable value realization (eg, process cycle-time reduction or cost-efficiency improvement)
Champion change and capability adoption, organizing enablement sessions, roadshows, and awareness programs to promote process-mining and analytics capabilities across business Region / functions
Support collaboration with academic and technology partners to explore new analytical methods and contribute insights to product enhancement discussions that inform Shell s Digital Process Twin (DPT) evolution
Mentor and coach data scientists, developing consulting, technical, and storytelling capabilities within the DPT community
Monitor industry trends and emerging technologies to provide input on potential future use cases in process analytics, optimization, and AI

What you bring

10 12 years of total experience, including 6+ years in analytics consulting, of which 3+ years in process-mining consulting or process-analytics leadership within global organizations or leading consulting firms
Bachelor s or Master s degree in Mathematics, Statistics, Economics, Data Science, Engineering, Technical Finance, or a related quantitative field
Combines deep analytical expertise with consulting leadership, leveraging prior hands-on experience in analytical solution development and data-driven problem solving to deliver measurable business outcomes
Process-Mining Technical Expertise: Strong command of process-mining concepts, algorithms, and tools (eg, Celonis EMS, SAP Signavio, PM4Py), with proven ability to extract, model, and interpret event-log data to uncover inefficiencies, monitor compliance, and optimize end-to-end processes
Led the development and delivery of AI/ML use cases using process-centric data, integrating predictive and prescriptive models with process-mining insights to drive data-informed decisions and measurable process transformation
Designed and implemented process data models and event-data integration frameworks, collaborating with process architects, data engineers, and IT teams to ensure analysis-ready, high-quality datasets across enterprise systems
Delivered process-analytics and AI-driven consulting engagements across business domains such as Finance, Sales, Procurement, and Supply Chain identifying bottlenecks, quantifying value opportunities, and achieving measurable performance improvements
Champions disciplined and responsible data-science delivery, applying Agile practices, MLOps/DevOps workflows (eg, GitHub, CI/CD), and Responsible AI (XAI) principles to ensure transparency, scalability, and measurable business outcomes across global teams
Demonstrated strength in analytical storytelling, distilling complex analytical findings into persuasive narratives and visual insights that drive executive alignment and decision-making
Experience in leading and active engagement in professional communities, industry forums, and technology meetups to foster collaboration, share insights, and drive innovation in analytical methods and digital transformation initiatives
Proven expertise in applying process mining, optimization, machine learning, or AI techniques to process centric data for measurable business outcomes
Strong proficiency in Python, SQL, and process data modeling, with experience transforming data from enterprise systems such as SAP, Salesforce, HANA, or ServiceNow
Deep understanding of process data structures (case IDs, activities, timestamps, attributes) and the ability to build scalable analytics pipelines
Structured, hypothesis-driven problem-solving approach with the ability to interpret and communicate analytical results clearly to diverse audiences
Ability to guide cross-functional teams, ensuring analytical solutions align with business strategies and deliver measurable value
Experience with cloud-based analytics environments and process-analytics platforms (eg, Celonis EMS, SAP Signavio, Microsoft Process Mining) and familiarity with major cloud platforms (Azure, AWS, GCP)
Understanding of end-to-end business-process lifecycles and value-chain optimization
Knowledge of process simulation, optimization, or Operations Research (OR) techniques for process improvement and decision support
Strong data storytelling and visualization skills, translating complex analytics into actionable business insights
Continuous-learning mindset, staying current with advances in process mining, AI/ML, and digital-twin technologies
Experience with complementary or open-source process-mining tools (eg,PM4PY, ProM)
Research and innovation orientation, contributing to proofs-of-concept or publications with academic and vendor partners

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

Data Science Engagement Lead

Job Description

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