About the role
We are looking for a skilled Machine Learning Specialist to join our Data Science team at BNP Paribas Wealth Management Asia to play a critical role in building, deploying, and optimizing machine learning 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 & Data 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
As a Machine Learning Specialist, you will collaborate with colleagues, data scientists, software engineers, and business stakeholders to develop scalable and efficient machine learning pipelines and AI systems that power predictive models, recommendations, and other AI applications aimed at improving client outcomes and optimizing internal processes.
Primary Role Responsibilities
• Model Deployment & Scaling:
Work closely with data scientists to take machine learning models from research and development to production. Design, implement, and maintain scalable, reliable, and high-performance machine learning pipelines to ensure models run efficiently at scale within the bank's ecosystem.
• AI Infrastructure & Tools:
Develop and optimize the infrastructure for machine learning applications, leveraging cloud technologies (AWS, Google Cloud, Azure) and distributed computing tools (e.g., Apache Spark, TensorFlow, PyTorch) to manage large datasets and support model training and deployment.
• Hyperpersonalization Enablement:
Support the development of hyperpersonalized banking services by integrating advanced personalization models, including recommendation systems, client segmentation algorithms, and predictive analytics tools that tailor financial products and services to individual client needs.
• Continuous Model Monitoring & Optimization:
Implement continuous monitoring of AI models in production, ensuring they are functioning as expected. Track model performance metrics, identify areas for improvement, and optimize models to adapt to changing data and client behavior.
• Automation of ML Processes:
Automate the end-to-end machine learning lifecycle, from data ingestion and preprocessing to model training, evaluation, and deployment, ensuring high efficiency, reproducibility, and minimal downtime.
• Collaboration Across Teams:
Work closely with colleagues to understand business requirements and translate them into technical solutions. Collaborate with software engineering teams in IT departments to ensure smooth integration of AI models into production environments, including client-facing applications, risk assessment tools, and backend systems.
• Data Engineering Integration:
Collaborate with IT departments to ensure that the data infrastructure is well-suited for machine learning tasks. Assist in building and maintaining data pipelines to collect, process, and store the data needed for model training and serving.
• Research & Innovation:
Stay up to date with the latest advancements in machine learning, AI, and data science. Explore new techniques and tools to improve model accuracy, scalability, and performance. Contribute to the development of innovative AI solutions that set the bank apart in the financial services industry.
• AI Model Documentation & Reporting:
Maintain clear and comprehensive documentation for machine learning models, their deployment processes, and performance evaluations. Communicate technical details to non-technical stakeholders and provide insights to guide future improvements.
About BNP Paribas
As the leading European Union bank, and one of the world's largest financial institutions with an uninterrupted presence in the region since 1860, BNP Paribas offers a wide range of financial services for corporate, institutional and private investors spanning corporate and institutional banking, wealth management, asset management and insurance.
We passionately embrace diversity and are committed to fostering an inclusive workplace where all employees are valued and encourage applicants of all backgrounds, including diversity of origin, age, gender, sexual orientation, gender identity, religion applicants who may be living with a disability. We have a number of internal employee networks in place to empower our staff to act and challenge the status quo.
• BNP Paribas PRIDE is highly active in favour of the LGBTQIA+ community
• BNP Paribas MixCity which fosters better representation of women at all levels of the organization
• Ability, the mutual aid network for employees with a disability or a disabling or chronic illness
• BNP Paribas CulturAll which celebrates diverse backgrounds
BNP is committed to financing a carbon-neutral economy by 2050. The Group is a founding member of the Net-Zero Banking Alliance and has set up its own Low Carbon Transition Group to support its clients through their energy transitions.
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