Data Scientist, Vice President

State Street’s Artificial Intelligence and Financial Engineering is a strategic global team. It is formed under direction of management committee of the company. It has the mission to explore, enable and exploit artificial intelligence, machine learning, natural language processing, computer vision and cognitive computing at scale for countless use cases across Global Delivery, Global Advisors, Global Markets, and Global Exchange business lines. The team has a mixed of intelligence technology, quantitative modeling, data science, financial engineering, and software engineering capabilities. The team has built solutions for investment and risk management related domains such as eFX trading, equity market movement, credit rating, sentiment analysis, valuation, investment strategy, fund innovation as well as operational and client experience related use cases and award winning product. This team will engage the business to explore, prototype, and solution use cases while also building out a portfolio of foundational micro services in the State Street’s public cloud.

The Vice President, AI and Machine Learning, will work with other AIFE agile team members through the phases of use-case data exploration and prototyping to the design, implementation and support deployment of the resulting AI services while focusing on scientific implementation and validation of quantitative financial models.

Responsibilities :

Responsible for delivery of assigned project and/or product from model design, development and validation to IT implementation and Quality Assurance Become subject matter expert in low level model and algorithm design, development, and validation by converting financial data and models to mathematical and computer science level design and implementation with consideration of space and time complexity Integrate, customize, and train well known model and algorithm in open-source libraries to solve financial market problem and perform validation and bug fixes Build models and algorithms from scratch and perform validation including but not limited to loss function, optimization, and parameter tuning depending on type of problem and technique used Drive model rationalization, reusability, scalability and implement modern MLOps Write model specification with lead financial engineer and modeler about problem statement, assumption, data input, methodology, implementation framework, and test result Support IT integration, QA/UAT and deployment of AI/ML micro services, operationalizing and productizing resulting models and cognitive solutions Support production issue pertinent to model and algorithm including the ones used in open-source libraries if any

Qualifications :

Master degree required (preferably in computer science, mathematical finance, and financial engineering), PhD from top ranked university preferred 7+ years of hands-on coding experience with classical AI/ML algorithms and familiar with deep learning neural network like various supervise and unsupervised models and comfortable with natural language processing and computer vision algorithms. 7+ years of solid modern, object-oriented or functional programming and design experience (Python, C++, SQL) Solid background in mathematics including but not limited to statistics, probability theory, PDE, linear algebra, stochastic calculus, differential equations etc. Familiar with public cloud development environment like Azure AML, Snowflake, or AWS SageMaker. Familiar with major financial instrument, reference data, market data, investment and risk management concept Fluent with English and Mandarin; Excellent written and verbal communication skills at all stakeholder levels across multiple countries Result driven, detail oriented, candid attitude

Experience in any of the following is highly desirable:

Prior data scientist role in financial institution and model validation experience Prior risk and investment management experience in asset management industry GenAI/LLMs, Data Science and Machine Learning Frameworks (TensorFlow, PyTorch, Scikit-learn,etc.) Linux / Bash scripting, structured and unstructured data management tools (Snowflake, Graph DB, PostgreSQL, Hadoop, etc.) Continuous integration & development environments and tools (Azure, AWS, GIT, Maven, Jenkins, etc.) Charted Financial Analyst (CFA)

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