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Tier-1 brand, mid-level Data Scientist title, metro location, and broad technical requirements raise applicant competition.
Core ML, MLOps, and cloud skills transfer across industries, but process-mining specialization raises domain specificity.
Explicit 6–8 years, mandatory process-event experience, and specific MLOps/process-mining skills make filters strict.
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Develop and deploy machine learning and optimization models on process event data to improve business process performance.
Design and maintain process data models and pipelines integrating data from enterprise systems for scalable process-mining analytics.
Collaborate with engineers and data scientists to embed AI/ML models into process-mining platforms and implement MLOps/DevOps workflows for model lifecycle management.
6–8 years of data science experience including 2+ years with process event or behavioral data.
Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, Mathematics, or related quantitative field.
Strong programming skills in Python and SQL; experience with process-mining platforms or frameworks such as Celonis EMS, PM4Py, or ProM.
Experience with ML lifecycle management tools (e.g., GitHub, CI/CD pipelines) and integration of AI/ML models into enterprise process environments.
Experienced in applying process-mining and AI/ML techniques specifically to large, multi-source enterprise event data to drive business insights and optimizations.
Skilled in designing and deploying robust, scalable analytical solutions within cloud or enterprise setups, with knowledge of MLOps and DevOps.
Demonstrates strong technical rigor including Responsible AI practices, and active engagement with innovation in process intelligence and digital process twins.