Machine Learning Specialist

What is this position about?

Responsibilities

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.

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 of machine learning models, including versioning, continuous integration/continuous deployment (CI/CD) for models, and automated testing of machine learning systems.

• Domain Knowledge:
  
  

Understanding of Wealth Management and financial services, with experience in areas such as risk management, fraud detection, wealth management, and client analytics is beneficial.

• Problem-Solving & Innovation:
  
  

Ability to think critically and creatively to solve complex problems and build innovative machine learning solutions that drive business value.

• Collaboration & Communication:
  
  

Strong teamwork skills and the ability to collaborate effectively with cross-functional teams. Excellent communication skills to explain technical concepts and outcomes to non-technical stakeholders.

Preferred Skills

• Experience in deploying and maintaining recommender systems, NLP models, or deep learning models.
• Familiarity with graph databases and graph-based machine learning techniques for fraud detection or social network analysis.
• Expertise in AI-powered automation, building & industrialization of AI-driven solutions in client-facing applications.
• Knowledge of AI model interpretability and explainability frameworks (e.g., SHAP, LIME).
• Experience with streaming data and real-time machine learning applications.
  
  

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.

https://careers.apac.bnpparibas/