Data Science Manager

Key Responsibilities

• Lead the design, architecture, and delivery of scalable Machine Learning, Operations Research (OR), and Process Mining solutions from business problem identification through production deployment.
• Collaborate with business stakeholders to conduct discovery workshops, define analytical requirements, and translate business challenges into data-driven solutions.
• Develop, deploy, and optimize advanced Machine Learning models, including classification, clustering, anomaly detection, time-series forecasting, graph analytics, and prescriptive optimization models.
• Apply process mining techniques to analyze event data, identify process inefficiencies, perform root cause analysis, and recommend improvements that enhance business performance and operational KPIs.
• Implement and maintain production-grade MLOps practices, including CI/CD pipelines, model versioning, experiment tracking, automated testing, monitoring, observability, Responsible AI (RAI), and Explainable AI (XAI).
• Partner with Data Engineers and Software Engineers to integrate AI/ML models into cloud-based enterprise platforms such as Celonis, Signavio, Databricks, and other real-time analytics environments.
• Define model evaluation frameworks, conduct performance validation through benchmarking, A/B testing, shadow deployments, and continuously monitor models to ensure accuracy, reliability, and business impact.
• Drive technical excellence by mentoring Data Scientists, conducting design and code reviews, promoting engineering best practices, and contributing to reusable frameworks, documentation, and knowledge-sharing initiatives.
• Collaborate with academic institutions, technology partners, and cross-functional teams to evaluate emerging technologies, lead proof-of-concepts (PoCs), and present data-driven insights and strategic recommendations to business leaders and executive stakeholders.

Required Skiils & Qualification

• Bachelor's or Master's degree in Computer Science, Data Science, Engineering, Mathematics, Statistics, or a related quantitative discipline.
• 6+ years of experience in Applied Data Science, Machine Learning, or Operations Research.
• Proven experience delivering at least three end-to-end production Machine Learning or Operations Research solutions with measurable business impact.
• Strong understanding of software engineering principles and production-grade ML system development.
• Excellent analytical, communication, stakeholder management, and problem-solving skills.
• Ability to work independently while collaborating effectively across cross-functional teams.