Vice President, Financial and Quant Engineering

7 days ago

Singapore State Street Full time

State Street’s Artificial Intelligence and Financial Engineering(AIFE) is a strategic global team. It has the mission to explore, enable and exploit artificial intelligence, machine learning, natural language processing, image recognition and cognitive computing at scale for countless solutions across Alpha, Global Services, Global Advisors, Global Markets, and Enterprise Risk Management business lines. The team is envisioned to have a mixed of intelligence technology, quantitative modeling, data science, financial engineering, and software engineering capabilities. The team started with solving operational and client experience related use cases and is advancing to investment and risk management related domains such as investment data quality control, digital marketing, sentiment analysis, valuation, investment strategy and so on. This team will engage the business to explore, prototype, and solution use cases while also building out multiple industry leading platforms in the public cloud.

The Financial and Quantitative Engineering Principle
- VP will lead one of our AIFE agile sub-teams by 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 Cognitive services into production.

**Responsibilities**:

- Become Subject Matter Expert in various public and private capital markets of asset management industry such as security terms and condition, security valuation models, pricing, analytics, market data, portfolio construction, optimization and rebalancing, and algorithmic trading
- Perform hands on fundamental and quantitative analysis, feature selection and engineering, model methodology, assumption, specification, design, implementation, and validation
- Design and program automated data collection and pre & post transformation pipeline
- Design financial framework and code financial model to address business problem and perform model validation
- Write model specification documentation with lead data scientist and quantitative modeler.
- Support IT integration, QA/UAT and deployment of AIFE models, operationalizing and productizing resulting models and cognitive solutions

Qualifications:

- Master degree required (preferably in financial engineering, mathematical finance, operational research, finance, economics and other engineering fields), PhD preferred
- 10+ experience with security terms and conditions, market data, security valuation modeling, performance attribution, portfolio optimization, or fund accounting and administration.
- 10+ years hands on experience with traditional buy side quantitative methodology and model that well understood by industry professional like portfolio manager, trader, risk manager, client, and regulator
- 8+ years of modern, object-oriented or functional programming experience (Python, Java, C++, SQL)
- Solid background in mathematics in general 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 or AWS SageMaker
- Proven experience as a Portfolio Analyst, Research Analyst or Financial Engineer
- 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 engineering or modeling team in medium or large size buy or sell side firm
- In-depth factor level modeling such as interest rate, spread, FX, momentum, sector, technical and fundamental indicators
- Data Science and Machine Learning Frameworks (TensorFlow, PyTorch, Scikit-learn etc.)
- Linux / Bash scripting, structured and unstructured data management tools (Snowflake, PostgreSQL, Hadoop, etc.)
- Strong analytical skills. Previous experience or education focused on statistics or data science is valuable.
- Communication skills. The ability to communicate at the right level with all parties involved, including management and business stakeholders
- Charted Financial Analyst (CFA) or CPA

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