Senior Quantitative Financial Engineer

4 weeks ago

Irvine, California, United States State Street Bank Full time
Job Description

State Street's Artificial Intelligence and Financial Engineering team is a global award-winning group with a mission to explore, enable, and exploit artificial intelligence, machine learning, natural language processing, image recognition, and cognitive computing at scale for various use cases across the company's business lines and corporate functions.

The team has a diverse range of capabilities, including intelligence technology, quantitative modeling, data science, financial engineering, and software engineering. They have successfully solved operational and client experience-related use cases and are expanding into investment and risk management domains such as market movement, sentiment analysis, valuation, investment strategy, and fund innovation.

The Financial and Quantitative Engineering Principle – VP will lead one of our AIFE agile teams through the phases of use-case identification, fundamental and quantitative analysis, data exploration, model specification, design, implementation, validation, and support integration and deployment of the resulting model services into production.

Responsibilities
  • Become a subject matter expert in financial business of asset management industry such as investment strategy and product offering research, portfolio construction, optimization and rebalancing, security selection, rich and cheap analysis, market and sector analysis, and algorithmic trading.
  • Lead a team of junior financial and quantitative engineers while being very hands-on in research and development on a daily basis.
  • Lead and mentor junior team members in fundamental and quantitative analysis, feature selection and engineering, model methodology, assumption, specification, design, implementation, and validation.
  • Engage with users and analysts to explore and prototype front office opportunities and use cases, exploring data and the application of cognitive and machine learning technology.
  • Design and program automated data collection and pre & post transformation pipelines.
  • Design financial frameworks and program models to address business problems and perform model validation.
  • Write model specification documentation with lead data scientists and quantitative modelers.
  • Support IT integration, QA/UAT, and deployment of Cognitive micro services, operationalizing and productizing resulting models and cognitive solutions.
Qualifications
  • Master's degree required (preferably in financial engineering, mathematical finance, operational research, finance, economics, and other engineering fields), PhD preferred.
  • Extensive experience with supporting investment research, portfolio management, and trading; good hands-on knowledge about market movement & sentiment analysis, portfolio optimization, security or factor-based investment strategy, cash flow analysis, or algorithmic trading.
  • 15+ years of experience with security terms and conditions, market data, security valuation modeling, performance attribution, portfolio optimization, or fund accounting and administration.
  • 15+ years of hands-on experience with buy-side quantitative methodology and models that are well understood by industry professionals like portfolio managers, traders, risk managers, clients, and regulators.
  • 10+ years of modern, object-oriented, or functional programming experience (Python, Java, C++, SQL).
  • Familiar with public cloud development environments like Azure AML or AWS SageMaker.
  • 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: leading a financial engineer or modeler team in a medium or large-size buy or sell-side firm, in-depth factor-level modeling, data science and machine learning frameworks, Linux/Bash scripting, structured and unstructured data management tools, strong analytical skills, previous experience or education focused on statistics or data science, communication skills, and the ability to communicate at the right level with all parties involved, including management and business stakeholders.

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