The Risk Economics Strats (RES) team is a central part of the team's risk management framework with primary responsibility for:
developing macroeconomic and financial scenarios for firm-wide scenario-based risk management.
developing and implementing statistical models for credit loss forecasting, business-as-usual risk management and regulatory stress testing requirements; and
analyzing large datasets of risk metrics to extract valuable insights about the firms exposures.
To fulfill these objectives, Risk Economics Strats interface with a wide array of divisional, finance and risk management groups across the firm. The cross-disciplinary nature of the projects that RES engages in makes for a challenging and multifaceted work environment.
RES professionals are part of the value proposition of the firm, and we balance our key functional responsibility of control and risk management with that of being commercial. RES has strong traditions of risk management, data analytics and career development opportunities for our people.
Responsibilities
Research, develop and implement coding infrastructure and environment to facilitate analysis related to scenario development, portfolio idiosyncratic risk, and credit loss impacts.
Understand, design, and implement models to capture different risk features critical for the estimation of credit loss impacts.
Apply statistical and machine learning techniques as required for risk management purposes.
Collaborate with other teams to understand different use-cases in order to develop and refine models.
Document loss forecasting, stressed capital models and methodologies for both internal and regulatory requirements.
Provide overall support to the team to meet requirements for regulatory stress-testing and business-as-usual risk management calculations.
Qualifications
Strong quantitative and analytical skills with advanced degree in a quantitative discipline with hands on experience in handling data, model building and programming.
Background in financial modeling, econometric modelling is preferred.
Ability to quickly learn and utilize quantitative modeling techniques.
Excellent written and verbal communication skills.
Strong organizational skills, strong communication and the ability to manage multiple assignments concurrently.
Prior Work Experience
At least 5-7 years of experience in financial modeling, loss fore-casting and business analytics related roles.
Experience with statistical techniques including segmentation, decision trees and other advanced risk predictive modeling methods.
Acquainted with statistical packages (Python, R, etc.) is required, elementary knowledge of data mining and data manipulation tools on big data platforms (Hadoop, Spark, etc.) will be preferred.
Any experience with building models for credit ratings , PD / LGD models and loss forecasting will be given preference.
Strong writing, presentation, and communication skills; technical writing and documentation experience desired
Ability to manage multiple assignments concurrently across various stakeholders.
Ability to be operate independently with high degree of ownership and accountability.
