Defines, develops, maintains and delivers strategic data science products that demonstrate the value and effectiveness of the organization. May allocate/coordinate work within a team/project. Develop and communicate insights leveraging the scientific method. Roles may specialize into 3 domain specialties : model science, feature science and Insight science capabilities. Accountable for developing and guiding more junior members.Defines, creates and maintains a meaningful insights. Establishes relationships to obtain data and subject knowledge needed to support advanced analytics. Conducts studies to provide additional facts needed to make informed decisions with regard to organizational and functional effectiveness with data decisioning.Communicates effectively with technical and business staff. Develops reports, and prepares and delivers both informational and decision-seeking presentations.Stays abreast of organization and management changes and has advanced knowledge of company practices relevant to data science products.Maintains knowledge of company's total computing environment and planned changes in order to develop meaningful data science products.Supervises resources by providing instruction, making assignments, directing and checking the work of applications developers. Provides training in technical tools and skills, as well as specific applications and their business functions to maximize their contribution.Grow and develop skills across the 3 domain specialties: model science, feature science and Insight science capabilities. Stressing expertise in the core functional areas: Computer Programming, Math&Analytic Methodology, Distributed computing and communications of complex results.Contributes to the achievement of related teams' objectives
Comprehensive Capital Analysis & Review (CCAR) is a Federal Reserve requirement to perform stress testing to demonstrate banks have sufficient capital to withstand a severe economic stress event; results of the CCAR process inform firm’s ability to take capital actions such as paying dividends and share repurchases. Current Expected Credit Losses (CECL) is an approach used to calculate loan loss reserves reported on firm’s SEC financial statements (e.g., 10K, 10Q). CCAR Tech Team implements and executes CCAR and CECL financial analytics models covering forecasting (Balance Sheet, Macroeconomic Factors, Expense and Fee Revenue, etc.) and calculation (Market & Credit Risks, Risk Weighted Assets, Stress Capital Buffer Ratio, etc.) using analytics platform (compute cluster, GPU cluster, Spark/Hadoop, JupyterHub, Machine Learning(ML) toolkits, REST API, Micro-Services).
We have an exciting opportunity for a talented Data Scientist to join our team in Pittsburg, PA/New York, NY. As data scientist within the CCAR Tech team, you will be working in a fast-paced environment to implement and provide risk model execution support for CCAR/CECL and our analytics platforms. You will gain a thorough understanding of CCAR/CECL risk models and related financial analytics, and use your skills and expertise for implementing risk models and investigating and supporting financial analytics work.
- Collaborate with stakeholders throughout the organization to develop project plans of delivering objects and timelines of risk model development and implementation.
- Implement and develop risk models in Python for regulatory stress testing submission and company risk management. Design and build the execution workflow of models to forecast Balance Sheet, Fee Revenues, Macroeconomic Factors, Expense and calculate risk metrics under various stress scenarios, sensitivity & attribution analysis.
- Coordinate with business quants and functional users to implement models and coordinate coding, testing, implementation and documentation of financial models for CCAR/CECL, including credit risk, market risk, RWA and balance sheet forecasting models.
- Develop processes and tools to monitor and analyze model performance to ensure the expected application performance levels are achieved. Also, execute enterprise standards for model validation by applying statistical techniques and methodologies to test assumptions and review results of models.
- Develop presentation decks using visual analytics tools and techniques. (JupyterHub/Python)
- Apply data mining, data modelling and machine learning techniques to analyze large financial datasets and enhance the model performance.
- Bachelor/Master/PhD's Degree in a quantitative discipline, including computer science, financial engineering, mathematics, statistics, data science and engineering.
- 6-8 years of experience in a related field is required for PhD Candidates. 3-5 years of experience in a related field is required for Master/Bachelor Candidates.
- Experience with complex quantitative modeling, numerical analysis, and computational method using one or more programming languages (Python, R, C++, Java, Matlab, etc.) and statistical/data manipulating software packages (Pandas, Scikit-Learn, MatPlotLib, SQL, etc)
- Knowledge of advanced statistical techniques and concepts (regression, time series analysis, statistical models, etc.) is preferred
- Experience working with market risk, credit risk or treasury risk models and financial products is preferred.
BNY Mellon is an Equal Employment Opportunity/Affirmative Action Employer. Minorities/Females/Individuals with Disabilities/Protected Veterans. Our ambition is to build the best global team – one that is representative and inclusive of the diverse talent, clients and communities we work with and serve – and to empower our team to do their best work. We support wellbeing and a balanced life, and offer a range of family-friendly, inclusive employment policies and employee forums.