Data Science Engagement Lead at Shell (Closed) | chakrie

Data Science Engagement Lead

SHELL CENTRE – CHENNAI
Full time
Work at office
Closed

Salary: Negotiable
Closed on: 5 Oct 2026Closed
Vacancy: Not specified
Category: Others

Applications closed

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Summary

Vacancy: Not specified
Location: SHELL CENTRE – CHENNAI
Salary: Negotiable
Experience: Lead / Manager
Employment Status: Full time
Workplace: Work at office
Published: 27 Oct 2026

Requirements

Experience

Experienced Professionals

Additional Requirements

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 (e.g., 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 (e.g., 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 (e.g., 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 (e.g.,PM4PY, ProM).
Research and innovation orientation, contributing to proofs-of-concept or publications with academic and vendor partners.

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Summary

Requirements

Experience

Additional Requirements

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