Data Scientist at Shell (Closed) | chakrie

Data Scientist

Chennai - RMZ Millenia
Full time
Work at office
Closed

Salary: Negotiable
Closed on: 5 Oct 2026Closed
Vacancy: Not specified
Category: IT & Telecommunication

Applications closed

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Summary

Vacancy: Not specified
Location: Chennai - RMZ Millenia
Salary: Negotiable
Experience: Mid Level
Employment Status: Full time
Workplace: Work at office
Published: 27 Oct 2026

Requirements

Experience

Experienced Professionals

Additional Requirements

6–8 years of experience in data science, including hands-on development of AI/ML or optimization models and at least 2 years working with process event or behavioral data.
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).

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Summary

Requirements

Experience

Additional Requirements

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