Senior Data Scientist

What is this position about?

We are seeking an experienced Senior Data Scientist with a strong background in AI and Data Science to join our Data Science team at BNP Paribas Wealth Management Asia. This is an exciting opportunity to play a pivotal role in establishing our Asia AI Center of Excellence (COE) under the leadership of the Chief Digital & Data Officer. In this high-impact role, you will leverage cutting-edge machine learning and AI techniques to drive data-centric innovations in wealth management with the following key objectives:

- Define, prioritize & execute our AI strategy & plan, Maximize AI’s value creation
- Identify key technologies & partners to design & implement the entity’s AI governance,
- Build its tech stack, workflows, processes & standards for development & industrialization
- Leverage the Group AI ecosystem to optimize synergies & re-use of common, standardized tech stacks
- Under a “One Bank” approach, develop collaborations with other Group entities (CIB, AM & Retail banks).
- Promote a “analytics” & “AI everywhere, for everyone” mindset for all BNPP Wealth Management staff

Your expertise will be crucial in shaping the AI strategy, developing advanced analytics models, and ensuring that AI technologies are embedded seamlessly into various client-facing and business functions. This is a chance to be part of a transformative journey as we set the foundation for AI-driven solutions in our Bank.

Primary Role Responsibilities
- AI Center of Excellence Development:
Partner with the Chief Digital & Data Officer and senior leadership to build and expand the AI Center of Excellence (COE), setting the strategic direction for AI adoption across the bank. Champion best practices, governance, and ethical AI frameworks, ensuring that AI initiatives align with the bank’s broader digital transformation objectives.
- Industrialization of our AI Priorities:
Drive AI-powered initiatives by developing advanced machine learning models that tailor banking services and financial advice to the unique preferences, behaviors, and needs of each client and staff. Utilize predictive analytics to forecast client behaviors, market trends, and potential risks. Build advanced models for credit risk scoring, churn prediction, and portfolio optimization that enable proactive decision-making and enhance financial outcomes. Leverage deep learning, recommender systems, and predictive analytics to deliver individualized experiences that improve overall satisfaction and loyalty.
- AI Model Development & Deployment:
Lead the design, development, and deployment of machine learning models, including deep learning, reinforcement learning, and natural language processing (NLP), to address business challenges in client segmentation, wealth management, fraud detection, and client experience optimization.
- Collaboration Across Functions:

- Leadership and Mentorship:
Provide mentorship and foster a culture of innovation, knowledge-sharing, and continuous learning with peers & colleagues. Bring the entire workforce on the journey of AI for upskilling/ re-skilling.
- Data Strategy & Data Quality:
Ensure that high-quality, accurate, and reliable data is available for building AI models. Establish best practices for data collection, data cleaning, and data governance. Collaborate with other data-related functions to build scalable data pipelines for model training and deployment.
- AI Ethics & Compliance:

- Continuous Model Monitoring & Optimization:
Implement robust monitoring systems to track the performance of AI models and adjust for evolving client needs, business requirements, and market conditions. Optimize models in real-time based on feedback loops and ongoing performance data.
- Innovation & Research:
What is required for you to succeed?
- Education:
Master’s or Ph.D. in Computer Science, Data Science, Statistics, Mathematics, or a related field.
- Experience:
5 to 7+ years of experience in data science, with at least 2 years in a senior or leadership role, preferably in financial services, banking, or technology sectors. Experience with AI implementation/industrialization in a client-centric environment (e.g., personalization, recommendations) is a strong advantage. Good knowledge of agile methodologies, design thinking, Test&Learn & A/B testing approaches
- Advanced Technical Expertise:

- Strong proficiency in machine learning frameworks (e.g., TensorFlow, PyTorch, Scikit-learn) and libraries for model development.
- Deep expertise in deep learning, reinforcement learning, and natural language processing (NLP), particularly for developing recommendation systems and personalization models.
- Strong programming skills in Python, R, and SQL. Experience with big data technologies (Hadoop, Spark) and cloud platforms (AWS, Google Cloud, Azure).
- Expertise in building and optimizing end-to-end machine learning pipelines and managing model deployment at scale.
- Good knowledge in Generative AI specific skills: promp