Director of AI (Center of Excellence)

About the role

BNP Paribas is looking for a strategic, impact-driven Data Scientist (Director level) to shape its AI and Generative AI strategy and implementation in Wealth Management Asia. The role bridges AI research and large-scale business use, co-steers the AI Center of Excellence (CoE) and acts as a strategic navigator across the wider group. It combines deep technical mastery of machine learning and generative AI with strong emotional intelligence and organizational savvy.

Key responsibilities

• Define, prioritize and execute the AI strategy, identify key technologies and partners, and build the CoE's tech stack, workflows, processes and standards
• Collaborate with the Chief Digital & AI Officer and senior leadership, and work with other group entities (CIB, AM, Retail) under a "One Bank" approach
• Develop machine learning models for client segmentation, credit risk scoring, churn prediction, portfolio optimization, fraud detection and personalized recommendations
• Operationalize GenAI through secure APIs, model orchestration and enterprise-grade architecture, with end-to-end lifecycle management and continuous monitoring
• Work with Front Office, Investment Services, Risk, Compliance and Operations, and with Legal, Risk and Compliance on ethics, transparency and GDPR compliance
• Drive an "AI everywhere" mindset through mentorship, upskilling and partnerships with tertiary institutions
• Ensure high-quality data, establish data governance, and set GenAI evaluation metrics such as toxicity, factuality and response quality

Required profile

• Master's or Ph.D. in Computer Science, Data Science, Statistics, Mathematics or a related field
• 12+ years in AI/ML, with at least 5-7 years in a senior or leadership role, preferably in financial services, banking or technology
• Proven ability to lead cross-functional teams, influence executives and navigate a large, matrixed global organization
• Deep knowledge of banking, wealth management, client analytics, risk management and fraud detection

Required skills

• Machine learning, deep learning, NLP and reinforcement learning; Python, SQL and cloud platforms (AWS, Azure, GCP)
• MLOps, LLMOps, CI/CD, and ML frameworks such as TensorFlow, PyTorch and Scikit-learn
• Generative AI: LLM frameworks, prompt engineering, RAG, vector databases and agentic workflows
• Preferred: familiarity with chatbots and virtual assistants, and data visualization tools such as Tableau, Power BI, Matplotlib or Seaborn