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State Street Corporation, one of eight G-SIFIs (global systemically important financial institutions) in the US, provides various financial products and services to institutional investors worldwide. State Street services over $28 trillion in assets, manages over $2.4 trillion in assets and has presence in the major financial centers around the world.
State Street recently formed Revenue Analytics group to develop actionable data to support business strategies. The group looks to redesign the data architecture to support a centralized global billing system. The group is highly entrepreneurial and headed by two MIT alums (Sloan MBA, PhD in Theoretical Physics). We are looking to create a group to develop the infrastructure to mine big data of G_SIFI, provide intelligent analysis, and deliver automated processes to position the bank with a unique edge in data driven strategies.
The candidate will work closely with the quantitative revenue finance team and IT. The candidate is expected to have strong programming skills that can be used to develop application-driven by data mining techniques, mathematical analysis, and statistical modeling. Knowledge of financial products like fixed income and derivatives will be a plus. This role requires the candidate to develop a comprehensive understanding of the business at the operation, processing, and decision making level. It also requires strong communications skills and ability to convey complex concepts to non-technical audiences. The ideal candidate will need to be inquisitive, show strong interest in understanding the underlying business, and being able to apply her/his analytical skills to architecture and develop big data solutions. More importantly, the role is highly entrepreneurial and the potential candidate will be expected to be creative, dedicated, independent, and be motivated by application of technology for financial analysis.
- Develop and evaluate current data infrastructure and billing systems
- Analyze business processes to identify data needs for global billing project
- Develop and evaluate statistical models used for finance, data processing, and forecasting
- Design and implement IT infrastructure for automated processes based on analytical models and data classification algorithms
- Implement pattern recognition methodologies for predictive analytics
- Develop and maintain relationships with finance and accounting professionals throughout the corporation (domestic and international)
- Master in Financial Engineering or a Ph.D. in a quantitative field or substantial equivalent experience
- Experience in large data management systems and algorithms
- Experience with statistical packages and programming skills (Matlab, SQL, SAS, C++, Python) a must
- Knowledge or experience with financial pricing and/or financial risk models
- Knowledge of machine learning algorithms
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