Develop and implement machine learning and optimization models using process event data to predict classify and enhance business-process performance.
Apply process-mining and AI/ML techniques to discover process patterns identify root causes and quantify improvement opportunities.
Implement and maintain process data models integrating and transforming data from enterprise systems (e.g. SAP Salesforce ServiceNow) into structured analysis-ready pipelines for process-mining and machine-learning applications.
Collaborate with senior data scientists data engineers and software developers to embed models into Shell s process-mining platform for scalable real-time analytics.
Develop algorithms that combine process-mining insights with AI/ML models for anomaly detection forecasting and prescriptive optimization.
Implement MLOps and DevOps workflows for model lifecycle management version control deployment and monitoring using GitHub and CI/CD pipelines.
Support the establishment of best practices in solution design data quality and delivery of scalable analytical products aligned with strategic business objectives.
Contribute to research and innovation by evaluating emerging tools algorithms and open-source frameworks in process intelligence and digital twins.
Ensure Responsible and Explainable AI (XAI) principles are embedded in all model development and deployment practices.

What you bring

6 to 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.
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.
Bachelor s or Master s degree in Computer Science Data Science Engineering Mathematics or a related quantitative field.
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.

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