Vista previa de la oferta
Director, Data Scientist (Applied AI, AI Hub Singapore)
senior · Artificial Intelligence
We’re seeking a future team member for the role of Director, Data Scientist – Applied AI to join our AI Hub team. This role is located in Singapore.
This is a founding leadership role for AI Hub’s new Singapore group. The Director, Data Scientist – Applied AI will establish and lead a team across applied AI, data science, AI engineering, and product-aligned delivery disciplines, creating a strategic APAC node for BNY’s enterprise AI platform, Eliza.
This role is intended for a senior applied AI leader who brings strong data science depth, practical experience delivering AI solutions, and the ability to translate complex business problems into production-ready AI capabilities. The successful candidate will understand AI like a data scientist, operate with the discipline of an enterprise technology leader, and partner closely with product and business stakeholders to shape roadmaps, prioritize opportunities, and deliver measurable outcomes.
The Director, Data Scientist – Applied AI will work across data science, engineering, product, operations, risk, governance, and APAC business stakeholders to identify high-impact AI opportunities and convert them into scalable solutions. This role requires technical depth in AI/ML, strong leadership, hands-on judgment, disciplined execution, and the ability to build a new team and operating model from the ground up.
In this role, you’ll make an impact in the following ways:
- Establish and lead AI Hub’s Singapore applied AI group, including hiring, onboarding, mentoring, and developing a team across data science, AI engineering, and product-aligned delivery roles.
- Define the Singapore team’s charter, operating model, delivery cadence, technical quality bar, and ways of working in alignment with global AI Hub strategy.
- Serve as the senior applied AI and data science leader for AI Hub in Singapore, representing AI Hub across APAC business, technology, product, risk, and governance stakeholders.
- Lead applied AI solution development from problem framing, data understanding, experimentation, prototyping, and model evaluation through production deployment, adoption, monitoring, and ongoing improvement.
- Partner with Product Managers and Product Owners to shape AI roadmaps, prioritize high-value opportunities, define requirements, and ensure AI capabilities deliver measurable business outcomes.
- Bring deep technical judgment to AI solution design, including evaluation of model architectures, retrieval-augmented generation approaches, prompt strategies, agentic workflows, model performance, scalability, cost, and risk tradeoffs.
- Translate complex business problems into practical AI solution patterns, data science approaches, technical designs, success metrics, and implementation plans.
- Establish rigorous AI evaluation practices, including model quality measurement, hallucination and factual grounding assessment, human-in-the-loop review, feedback loops, monitoring, and continuous improvement.
- Partner with engineering, platform, architecture, operations, risk, governance, and business teams to ensure AI capabilities are scalable, secure, reliable, explainable, observable, and aligned to enterprise standards.
- Embed responsible AI practices into the Singapore group’s delivery model, including transparency, governance readiness, model risk considerations, control alignment, and appropriate human oversight.
- Drive adoption and enablement of AI capabilities across APAC business lines through demos, workshops, stakeholder engagement, training, and performance measurement.
- Contribute to global AI Hub standards, including reusable solution patterns, reference architectures, evaluation frameworks, documentation, and communities of practice.
To be successful in this role, we’re seeking the following:
- Preferred 12+ years of experience across a mix of engineering, product management, data science, applied AI, machine learning, advanced analytics, data products, or enterprise technology delivery roles.
- Strong applied experience with Artificial Intelligence, Generative AI, Machine Learning, large language models, NLP, retrieval-augmented generation, agentic systems, AI evaluation, model governance, and enterprise AI delivery.
- Demonstrated ability to lead applied AI initiatives from discovery and experimentation through production deployment, adoption, monitoring, and measurable value realization.
- Ability to critically evaluate AI system designs, including model selection, prompt strategies, RAG architecture, evaluation methodology, hallucination risk, factual grounding, human oversight, scalability, and cost / quality tradeoffs.
- Proven experience translating complex business problems into AI approaches, data science solutions, technical requirements, success metrics, and delivery plans.
- People leadership experience, including hiring, mentoring, performance management, technical quality bar setting, and leading teams in a matrixed organization.
- Experience partnering with product, engineering, operations, risk, governance, and business stakeholders to deliver production-grade AI, analytics, or technology solutions.
- Strong understanding of responsible AI, model risk, data controls, privacy, security, operational resiliency, and governance expectations in enterprise environments.
- Strong communication, stakeholder management, and executive presentation skills, with the ability to explain complex AI concepts to both technical and non-technical audiences.
- Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Data Science, Engineering, Mathematics, Statistics, or a related quantitative field, or equivalent experience. Advanced degree preferred.
Preferred qualifications:
- Experience in financial services, enterprise technology, fintech, or another regulated environment.
- Experience leading applied AI teams or AI solution delivery in APAC or across global / regional operating models.
- Hands-on experience or deep working knowledge of LLM evaluation, model fine-tuning, prompt engineering, AI governance, model risk management, data governance, or responsible AI frameworks.
- Familiarity with AI observability, drift detection, production AI operations, model performance monitoring, data quality, and cost optimization.
- Experience working with Product Managers or Product Owners to shape AI product roadmaps, prioritize use cases, define adoption metrics, and measure business value.
- Experience with Agile delivery at scale, pod-based operating models, backlog management, product release practices, and enterprise platform adoption.