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Tier-1 brand, metro location, mid-level generalist title, and broad skillset increase candidate competition.
Strong Order-to-Cash, ERP, and domain-specific process mining requirements increase industry-specific fit sensitivity.
Explicit years, mandatory people-management and specific tools imply stringent screening.
Lead and manage a team of data scientists and analysts to develop and deploy data-driven solutions improving Sales Order Management processes and Order-to-Cash operations globally.
Own strategy and governance of the Sales Order Management Business Rules Engine, ensuring optimization of rules to reduce defects and rework with measurable outcomes.
Drive predictive analytics, AI model development, process automation, and operational excellence initiatives to enhance order quality, reduce cycle times, and increase automation adoption.
5 to 10 years of experience in Data Science, Advanced Analytics, Process Optimization, or Automation.
Minimum 3 years of people management experience.
Experience supporting Order-to-Cash (O2C), Sales Order Management, or Customer Operations.
Technical proficiency in Python, SQL, Pyspark, data visualization tools, ML frameworks (Scikit-learn, XGBoost, TensorFlow/PyTorch), process mining tools (e.g., Celonis), and workflow automation/RPA platforms (UiPath, Power Automate, etc.).
Experienced leader in data science and operational analytics within financial services or related industries focusing on Order-to-Cash or Sales Order Management processes.
Proven ability to translate business operational requirements into automated decision rules and scalable AI/automation solutions that deliver measurable business impact.
Skilled at stakeholder management across multiple functions including Sales, Finance, Technology, and Compliance to drive end-to-end process improvements and digital transformation.