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Shell

Global oil and gas energy company

Data Science Engagement Lead

Data ScienceUpdated on Nov 13
Full-timeChennai, Tamil Nadu, IndiaNo salary listedIn-person

About the Job

Requirements

  • 10–12 years of total experience, including 6+ years in analytics consulting and 3+ years in process-mining consulting or process-analytics leadership within global organizations or leading consulting firms.
  • A Bachelor's or Master's degree in Mathematics, Statistics, Economics, Data Science, Engineering, Technical Finance, or a related quantitative field.
  • Deep analytical expertise and consulting leadership, supported by hands-on analytical solution development and data-driven problem solving to deliver measurable business outcomes.
  • Strong command of process-mining concepts, algorithms, and tools, including Celonis EMS, SAP Signavio, and PM4Py, with ability to extract, model, and interpret event-log data to uncover inefficiencies, monitor compliance, and optimize end-to-end processes.
  • Experience leading development and delivery of AI/ML use cases using process-centric data, integrating predictive and prescriptive models with process-mining insights to support decisions and measurable transformation.
  • Experience designing and implementing process data models and event-data integration frameworks with process architects, data engineers, and IT teams to provide analysis-ready, high-quality enterprise-system datasets.
  • Experience delivering process-analytics and AI-driven consulting engagements across domains such as Finance, Sales, Procurement, and Supply Chain, identifying bottlenecks, quantifying value opportunities, and achieving measurable performance improvements.
  • Experience applying Agile practices, MLOps/DevOps workflows such as GitHub and CI/CD, and Responsible AI (XAI) principles to support transparent and scalable data-science delivery.
  • Strength in analytical storytelling, presenting complex findings as persuasive narratives and visual insights for executive alignment and decision-making.
  • Experience engaging in professional communities, industry forums, and technology meetups to foster collaboration, share insights, and advance analytical methods and digital transformation.
  • Expertise 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, including transforming data from enterprise systems such as SAP, Salesforce, HANA, or ServiceNow.
  • Deep understanding of process data structures, including case IDs, activities, timestamps, and attributes, and ability to build scalable analytics pipelines.
  • Structured, hypothesis-driven problem-solving approach and ability to interpret and communicate analytical results to diverse audiences.
  • Ability to guide cross-functional teams and align analytical solutions with business strategies and measurable value.
  • Experience with cloud-based analytics environments and process-analytics platforms such as Celonis EMS, SAP Signavio, or Microsoft Process Mining, and familiarity with Azure, AWS, or GCP.
  • Understanding of end-to-end business-process lifecycles and value-chain optimization.
  • Knowledge of process simulation, optimization, or Operations Research techniques for process improvement and decision support.
  • Strong data storytelling and visualization skills for translating analytics into actionable business insights.
  • Ability to stay current with advances in process mining, AI/ML, and digital-twin technologies.

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 process data-model design and development, collaborating with process and data architects to ensure clean, integrated, 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 data storytelling.
  • Guide delivery of analytical and AI/ML solutions in line with strategic priorities, technical excellence, and measurable value realization, such as 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.
  • Collaborate with academic and technology partners to explore analytical methods and contribute insights to product-enhancement discussions informing the Digital Process Twin evolution.
  • Mentor and coach data scientists, developing consulting, technical, and storytelling capabilities within the Digital Process Twin community.
  • Monitor industry trends and emerging technologies and provide input on potential future process-analytics, optimization, and AI use cases.

Desired Qualifications

  • Experience with complementary or open-source process-mining tools such as PM4Py and ProM.
  • Research and innovation orientation, including contributing to proofs of concept or publications with academic and vendor partners.

About the Company

Shell

Global oil and gas energy company

Provide a concise summary of Shell that answers: 1) what they do, 2) how their products work, 3) how they differ from competitors, and 4) their goal, in simple terms suitable for a high school student.

Company size
10,001+
Company stage
IPO
Headquarters
London, United Kingdom
Founded
1890

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