Chief Data Scientist / AI Lead - Capital Markets Technology

SMBC•New York City, NY

14d•Hybrid

About The Position

SMBC Group is a top-tier global financial group with a 400-year history, headquartered in Tokyo and operating in nearly 40 countries with over 130 offices and 80,000 employees worldwide. In the Americas, SMBC Group has a presence in the US, Canada, Mexico, Brazil, Chile, Colombia, and Peru. The Group offers a range of commercial and investment banking services to its corporate, institutional, and municipal clients, connecting them to local markets and its extensive global network. The anticipated salary range for this role is between $300,000.00 and $325,000.00, with the specific salary dependent on individual qualifications, experience, and market analysis. The role may also be eligible for an annual discretionary incentive award and offers a competitive portfolio of benefits. We are seeking a Managing Director, Chief Data Scientist to lead the development and application of advanced AI, Machine Learning, and Large Language Model (LLM) solutions across Capital Markets. This role is for a hands-on technical leader with deep expertise in data science, fixed income markets, and front-office technology. The successful candidate will collaborate directly with Trading, Risk, and Quantitative teams to identify opportunities, rapidly prototype solutions, and deliver production-ready AI capabilities that drive measurable business impact. This senior role requires both strategic leadership and active hands-on development.

Requirements

PhD in Mathematics, Statistics, Physics, Computer Science, Engineering, or a related quantitative discipline required.
15+ years of experience in Data Science, Machine Learning, Quantitative Analytics, Artificial Intelligence, or related technology disciplines.
Proven track record designing, building, and deploying production-grade AI/ML solutions that deliver measurable business outcomes.
Expert-level proficiency in Python and modern AI/ML frameworks and platforms.
Deep expertise in: Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), Agentic AI workflows, Natural Language Processing (NLP), Machine Learning and Predictive Analytics, Statistical Modeling and Data Science.
Experience developing scalable data pipelines, real-time analytics platforms, and cloud-based AI solutions.
Demonstrated ability to partner with senior business stakeholders and translate complex business challenges into practical, scalable solutions.
Distinguished technical contributor with a hands-on approach to architecture, coding, model development, and problem solving.
Strong leadership and mentoring skills with the ability to guide engineers and data scientists through direct technical engagement.
Excellent communication skills with the ability to explain advanced analytical concepts to both technical and non-technical audiences.

Nice To Haves

Capital Markets experience, particularly within Fixed Income, Trading, Risk, or Quantitative Research, is helpful.
Professional experience from other data-intensive industries such as retail, technology, healthcare, telecommunications, or e-commerce will also be helpful.

Responsibilities

Lead the design, development, and deployment of AI/ML, LLM, RAG, NLP, and agentic AI solutions supporting trading, risk management, and client analytics.
Partner directly with traders, quants, and risk teams to transform business challenges into scalable technology solutions.
Apply deep knowledge of fixed income markets and the trade lifecycle, including trade capture, P&L, risk, settlement, and regulatory processes.
Rapidly prototype and productionize data science solutions that deliver tangible front-office business value.
Develop high-quality Python-based models and services using modern AI frameworks such as LangChain, Vertex AI, and related technologies.
Design and optimize real-time and batch data pipelines across trading, risk, and market data platforms.
Drive workflow automation, decision support, and exception management through advanced AI techniques.
Validate and refine models using trading data, market behavior, and user feedback.
Serve as a senior technical expert, providing hands-on problem solving for complex data, analytics, and technology challenges.
Mentor engineers and data scientists through direct coding, architecture guidance, and implementation leadership.