Our Banking Client is seeking strategic, forward-thinking and impact-driven Data Scientist (Director level) to be a key contributor in our AI and Generative AI (GenAI) strategy & implementation.
This role is ahigh-impact leadership role designed to bridge the gap between cutting-edge AIresearch and large-scale business implementation within Wealth Management Asia. This role is responsible for co-steering the roadmap of the AI Center of Excellence (CoE) while acting as a strategic navigator across the broader Bank.
Looking for someone who has experience in AI Transformation & Industrialization, some one who has implemented AI from scratch and experience in maximizing AI's value creation.
Role & Responsibilities:
- Strategic Steering & AI Center of Excellence Development:
- AI Strategy Leadership: Partner with the Chief Digital & AI Officer and senior leadership to define and execute an AI/GenAI strategy aligned with Wealth Management’s business goals.
- Use Case Orchestration: Identify, prioritize, and champion high-impact GenAI use cases (e.g., investment research synthesis, advisor enablement copilots, KYC document summarization, and hyper-personalized client engagement).
- 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.
- Lead the design, development, and deployment of machine learning models, includingd eep learning, reinforcement learning, and natural language processing (NLP),to address business challenges in client segmentation, wealth management, fraud detection, and client experience optimization
- AI Governance & Ethics: Partner with Legal, Risk, and Compliance to validate GenAI use cases, ensuring transparency, fairness, and strict adherence to data privacy and GDPR regulations.
- Ensure that high-quality, accurate, and reliable data is available for building AImodels. Establish best practices for data collection, data cleaning, and data governance.
- Ensure that AI applications comply with regulatory requirements and ethical standards, particularly in the context of Wealth Management, where sensitive client data and privacy concerns are paramount.
- 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.
Requirements:
- Master’s or Ph.D. in Computer Science, DataScience, Statistics, Mathematics, or a related field.
- 12+ years of experience in AI/ML, with at least5-7 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, designthinking, Test &Learn & A/B testing approaches
- Expertise: Deep knowledge of Machine Learning,Deep Learning, NLP, and Reinforcement Learning.
- GenAI Mastery: Strong command of LLM frameworks,Prompt Engineering, RAG, Vector Databases, and Agentic workflows.
- Engineering Fluency: Proficiency in Python, SQL,and cloud ecosystems (AWS/Azure/GCP), with a deep understanding of MLOps/LLMOps and CI/CD.
- 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: prompt engineering, RAG approaches, agentic AI, automated robustness and performance evaluation, production monitoring
- Strong understanding of banking and financial services, with a focus on areas such as wealth management, client behavior & analytics, risk management and fraud detection
- Experiencein defining AI strategies and embedding AI into an organization’s corefunctions. Ability to assess business needs and translate them into actionableAI-driven solutions.
- Familiarity with AI-driven automation for client service (e.g., chatbots, virtual assistants).
- Advanced data visualization skills using tools like Tableau, Power BI, or custom Python libraries (e.g. Matplotlib, Seaborn).
- Thought leader in the AI community, ability to build relationships with regulators and tertiary institutions will be crucial.
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