Advanced Analytics Project Leader
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Overview
In this role you lead end-to-end AI-driven analytics initiatives across the Group, working with cross-functional teams to design, build, and deploy advanced models and automation solutions. You shape business cases, define targets, and coordinate delivery through internal teams and external partners, ensuring impact on key business decisions. You will collaborate with business functions and IT architects to scale analytics services central to the platform. This role offers influence over transformative AI projects and direct interaction with management and stakeholders.
Responsabilità
- Launch and govern end-to-end AI/analytics initiatives, from scoping to delivery
- Analyze current processes, build business cases, and design target solutions
- Manage budgets, priorities, and internal/external providers; secure buy-in
- Lead data scientists in model development and supervise deployment with Automation Engineers/IT Architects
- Coordinate cross-functional collaboration with business units and management
- Ensure continuous improvement based on testing results and real-world impact
Requisiti fondamentali
- 5+ years as Data Scientist in consulting or industry
- 1–2 years project management experience in AI/digital transformation/operational excellence
- Degree in scientific disciplines (mathematics, computer science, engineering, economics, physics)
- Experience with Python data science libraries (Pandas, Scikit-learn, Numpy, XGBoost, Pytorch, LightGBM, Tensorflow)
- Experience designing, developing and evaluating ML models (clustering, classification, regression, forecasting)
- Excellent knowledge of Office, especially Excel
- Good written/spoken English (B2)
- Strong teamwork, autonomy, proactivity, commitment and results orientation
- Strong ability to communicate across organizational levels
- team collaboration
- proactivity
- strong communication
- Python data science libraries (Pandas, Scikit-learn, Numpy, XGBoost, Pytorch, LightGBM, Tensorflow)
- Statistical modeling and ML methods (clustering, classification, regression, forecasting)
- Model deployment coordination with Automation Engineers and IT Architects
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