Data Scientist

What is this position about?

We are looking for a skilled Data Scientist (VP/AVP) to join our team at BNP Paribas Wealth Management Asia to play a critical role in building, deploying, and optimizing AI models. 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 & AI Officer. You will be responsible for translating advanced AI and machine learning research into production-ready systems, enhancing the bank's ability to drive data-centric innovations, and integrating AI-driven solutions into the Wealth Management experience 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

Primary Role Responsibilities
- AI Center of Excellence Development:
Partner with senior leadership to build and expand the AI Center of Excellence (COE), setting the strategic direction and driving 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.
- Model Development and Deployment:
Design, develop, and deploy AI models that enable tailored investment journeys, optimized and hyper-personalized client interaction and engagement, and smart automation of complex processes e.g. KYC and Credit. Work with engineers and IT to deploy models into production, ensuring models run reliably and efficiently at scale within the bank’s ecosystem in production.
- 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 PhD in Computer Science, Data Science, Statistics, Applied Mathematics or a related field.
- Experience:
3 to 7+ years of experience in AI/ML with hands-on experience in developing and deploying AI models into production, preferably in the financial services or technology sector.

Experience in AI for investment/portfolio analysis and optimization, OR recommendations systems and personalization.

Good knowledge to expertise in Generative AI specific skills: prompt/context engineering, various RAG approaches, agentic AI, ability to understand & challenge data pipelines & architecture choices, anticipate key stakes in robustness and automated performance evaluation / prod monitoring.

Good knowledge of agile methodologies, design thinking, Test&Learn & A/B testing approaches.
- Technical Skills:

- Expertise in machine learning frameworks such as TensorFlow, PyTorch, Sklearn and relevant libraries like Pandas, Polars, Numpy
- Recommendation systems, Next Best Action, Pricing and personalization (particularly translation)
- Portfolio simulation and optimization, investment scenario modeling, risk and impact analysis
- Automation / augmentation of client service and/or credit, compliance (e.g. KYC, AML), or other operational processes
- Strong programming skills in Python, SQL and experience with cloud platforms (