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What is this position about?
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
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:
- 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 mínimal downtime.
- Collaboration Across Teams:
- 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.
What is required for you to succeed?
- Education:
Bachelor’s or Master’s in Computer Science, Engineering, Mathematics, Data Science, or a related field. Advanced degrees are a plus.
- Experience:
3 to 6+ years of experience in machine learning engineering, with hands-on experience in deploying machine learning models into production environments, preferably in the financial services or technology sector.
Good knowledge to expertise in Generative AI specific skills: prompt engineering (incl. Chain of Thought), 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, Scikit-learn, and Keras.
- Strong programming skills in Python, Java, or C++.
- Experience with cloud platforms (AWS, Azure, Google Cloud) and tools like Docker, Kubernetes/ K8s for model deployment and orchestration.
- Familiarity with big data technologies (e.g., Apache Spark, Hadoop, TaskQueue, ETL) for distributed model training and data processing.
- Solid experience in SQL and NoSQL databases (e.g., MongoDB, Cassandra, S3-type storage) and working with large datasets.
- Proficiency in data preprocessing, feature engineering, and model evaluation techniques.
- Machine Learning Operations (MLOps):
Hands-on experience with MLOps practices & tools (Prometheus, Grafana, Giskard) for managing the lifecycle
