New opportunity

Data Science Engagement Lead

Shell · Chennai, India

About this role

The Commercial Data Science (CDS) team within Shell's Digital Process Twin (DPT) portfolio is seeking a Data Science Engagement Lead to guide data scientists and collaborate with cross-functional business teams. In this role, you will identify transformation opportunities using process-centric data and oversee the application of process mining, optimization, and AI/ML techniques to improve operational performance and drive measurable business outcomes across global operations.

Responsibilities

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 such as SAP, Salesforce, and 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 like process cycle-time reduction or cost-efficiency improvement.
Champion change and capability adoption by organizing enablement sessions, roadshows, and awareness programs to promote process-mining and analytics capabilities across business regions and 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 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.

Required qualifications

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.
Strong command of process-mining concepts, algorithms, and tools such as Celonis EMS, SAP Signavio, and PM4Py, with proven ability to extract, model, and interpret event-log data.
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.
Demonstrated strength in analytical storytelling, distilling complex analytical findings into persuasive narratives and visual insights for executive alignment.

Benefits

Work with talented, committed people on global energy transition and digital innovation challenges.
Access a wide range of training and development programs and diverse career opportunities in national and international teams.
Benefit from a values-led culture that encourages work-life balance, flexibility, and a commitment to diversity and inclusion.

For full details about this role, click “Apply on company site” to view the employer’s original posting.

Skills for this role

PythonSQLCelonis EMSSAP SignavioPM4PyProMSAPSalesforceServiceNowHANAAzureAWSGCPGitHubCI/CD

Checking your profile…

Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.

Understand the role

What matters most

Process-Mining Consulting & Execution
Lead consulting engagements using tools like Celonis EMS, SAP Signavio, and PM4Py to extract, model, and interpret event-log data from enterprise systems (SAP, Salesforce, ServiceNow) to uncover inefficiencies and optimize end-to-end processes.
Cross-Functional Stakeholder Engagement
Partner with global and regional business stakeholders across domains like Finance, Sales, Procurement, and Supply Chain to uncover inefficiencies, define transformation opportunities, and facilitate discovery and value workshops.
AI/ML and Optimization Solution Delivery
Guide the delivery of analytical and AI/ML solutions, integrating predictive and prescriptive models with process-mining insights to achieve measurable outcomes like cycle-time reduction and cost-efficiency.
Team Mentorship & Capability Building
Mentor and coach data scientists within the Digital Process Twin community, developing their consulting, technical, and storytelling capabilities while championing change adoption across business functions.

Likely challenges to think through

Bridging Complex Process Data with Executive Storytelling
Difficulty translating highly technical process-mining models and event-log data from disparate enterprise systems into persuasive, simple narratives for executive stakeholders. Prepare by practicing structured data storytelling frameworks and mastering the ability to link technical bottlenecks directly to financial and operational KPIs.
Aligning Global Stakeholders Across Diverse Business Domains
Navigating competing priorities and varying process maturity levels across global and regional business units in domains like Finance, Supply Chain, and Procurement. Prepare by reviewing case studies on cross-functional alignment, stakeholder mapping, and structured workshop facilitation techniques.
Integrating AI/ML Solutions with Process-Centric Data Pipelines
Overcoming data quality issues and structural inconsistencies when integrating predictive and prescriptive AI/ML models with event-log data from systems like SAP, Salesforce, and ServiceNow. Prepare by reviewing scalable pipeline design principles and robust MLOps/DevOps practices.

Practice your answers

Technical practice

Process Mining Core Concepts and Event Logs

What to coverExplain how process-mining algorithms reconstruct event logs from enterprise systems like SAP or Salesforce. Candidates should discuss case IDs, activities, timestamps, attributes, and how handling messy or missing event data impacts process discovery accuracy.

Extracting and Modeling Event Data in Python and SQL

What to coverHow do you use Python and SQL to build scalable analytics pipelines from raw enterprise event tables? Guidance: Address data cleaning, structuring case-attribute tables, and utilizing libraries like PM4Py or Pandas to prepare data for process analysis.

Integrating AI/ML Models with Process-Centric Data

What to coverDiscuss how predictive and prescriptive machine learning models can be combined with process-mining insights to optimize operational workflows. Guidance: Focus on fusing event-log patterns with predictive features (e.g., cycle-time estimation) to drive measurable business value.

Celonis EMS and Process-Analytics Platforms

What to coverWhat are the key capabilities of enterprise process-mining platforms like Celonis EMS or SAP Signavio in identifying bottlenecks and compliance deviations? Guidance: Explain how conformance checking and variant analysis are leveraged to uncover operational inefficiencies.

Operations Research and Process Optimization Techniques

What to coverHow do you apply optimization or operations research techniques alongside process mining to support decision-making? Guidance: Discuss formulating objective functions and constraints based on end-to-end business process lifecycles and value chains.

Analytical Storytelling and Executive Alignment

What to coverHow do you distill complex process analytics and statistical findings into persuasive narratives for executive stakeholders? Guidance: Emphasize data visualization best practices, translating technical bottlenecks into financial metrics, and driving executive consensus.

Responsible AI and MLOps in Global Data Science Deliveries

What to coverWhat practices ensure transparency, scalability, and fairness when deploying AI/ML solutions across global teams? Guidance: Discuss MLOps/DevOps workflows (GitHub, CI/CD) and incorporation of explainable AI (XAI) principles.

Cross-Functional Integration with Enterprise Systems

What to coverHow do you collaborate with data architects and IT teams to ensure analysis-ready event data from systems such as HANA or ServiceNow? Guidance: Explain data governance frameworks, integration pipelines, and maintaining high data quality standards.

Behavioural practice

Leading a Complex Consulting Engagement

What to coverDescribe a situation where you led an analytics or process-mining consulting engagement with global stakeholders. Use the STAR method to detail how you defined problem statements, managed expectations, and delivered measurable outcomes.

Translating Technical Insights for Business Stakeholders

What to coverGive an example of a time you had to present complex analytical or AI findings to non-technical business leaders. Explain the situation, the storytelling or visualization techniques you used, and the resulting business alignment or process change.

Managing Cross-Functional Disagreements on Process Redesign

What to coverShare an experience where cross-functional teams disagreed on transformation opportunities or process bottlenecks. Outline how you facilitated workshops, used data-driven evidence to build consensus, and guided the team forward.

Mentoring and Coaching Data Scientists

What to coverDescribe a time when you mentored junior or peer data scientists to improve their technical, consulting, or storytelling capabilities. Detail your coaching approach and the observable professional growth that resulted.

Sharpen your knowledge

Topics to prepare

Process-Mining Concepts and Algorithms
Review core process-mining principles, including event-log data structures (case IDs, activities, timestamps, attributes), conformance checking, and bottleneck identification. Exercise: Analyze a sample event log using open-source tools to discover process variants and quantify cycle-time inefficiencies. Relevance: Essential for leading process-mining consulting engagements and interpreting operational workflows within Shell's Digital Process Twin portfolio.
Predictive and Prescriptive AI/ML Integration
Examine how machine learning and optimization models integrate with process-centric data to drive automated decision-making and operational improvements. Exercise: Design a conceptual framework combining a predictive delay model with process-mining insights to optimize supply chain lead times. Relevance: Directly tied to guiding the delivery of AI/ML solutions and process optimization use cases.
Analytical Storytelling and Stakeholder Engagement
Study frameworks for distilling complex data analytics into persuasive narratives and visual insights for executive alignment. Exercise: Prepare a 5-slide executive pitch translating technical process inefficiencies into quantified financial impacts. Relevance: Critical for facilitating process discovery workshops and driving executive buy-in across global business units.
Responsible AI and MLOps Workflows
Review principles of explainable AI (XAI), model transparency, and robust CI/CD and GitHub workflows for scalable data science delivery. Exercise: Document a checklist for auditing a machine learning model for bias and traceability in an enterprise setting. Relevance: Required for championing disciplined and responsible data science delivery across global teams.

Skills to revise

Process-Mining Tools (Celonis EMS, SAP Signavio, PM4Py)
Practice extracting, modeling, and interpreting event logs using industry-standard process-mining platforms and libraries. Exercise: Build an interactive process discovery dashboard in Celonis EMS or PM4Py using mock enterprise event data. Relevance: Directly addresses the core technical requirement for building and modeling process data within enterprise environments.
Python and SQL for Process Data Modeling
Refresh advanced data manipulation techniques in Python and SQL for transforming enterprise data structures. Exercise: Write optimized SQL queries and Python scripts to join SAP or Salesforce event logs and structure them into case-attribute tables. Relevance: Required for building scalable analytics pipelines and integrating data from enterprise systems like SAP, Salesforce, and ServiceNow.

Your preparation checklist

Review Process-Mining and Event-Log Fundamentals
Refresh core concepts of event-log extraction, case attributes, and conformance checking using tools like PM4Py, Celonis EMS, or SAP Signavio to ensure readiness for process-centric data discussions.
Prepare a Process-Mining Case Walkthrough
Assemble a structured presentation of a past analytics or process-mining project, detailing how you translated a business challenge into an analytical problem statement and achieved measurable performance improvements.
Brush Up on Python, SQL, and Enterprise Data Schemas
Review advanced data manipulation techniques in Python and SQL, focusing on structuring event logs and transforming data from enterprise systems such as SAP, Salesforce, or ServiceNow.
Draft an Analytical Storytelling Portfolio
Prepare visual examples and narrative frameworks that demonstrate your ability to distill complex analytical findings and machine learning results into persuasive insights for executive stakeholders.
Outline Leadership and Mentorship Examples
Prepare specific STAR-format examples highlighting your experience leading data science teams, coaching peers in technical and consulting capabilities, and driving change adoption across global organizations.
Formulate Targeted Employer Questions
Develop thoughtful questions regarding the Digital Process Twin (DPT) portfolio roadmap, collaboration models with business stakeholders, and the integration of AI/ML with process-mining initiatives at Shell.

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