Lead, Investment Risk Analytics
Job Title: Lead, Investment Risk Analytics
Full-Time
Location: New York, NY (as primary location), Boston, MA (as secondary location).
The Opportunity
As a lead investment risk quant within the Capital & Investment Risk Management team you will be involved with quantitative model implementation, development, and analysis. The ideal candidate will join a quant team and lead to enhance ERM’s analytical and reporting capabilities, by expanding the use of existing models as well as designing and developing new tools and risk frameworks.
You will work with capital, credit, market and portfolio risk teams, and ERM more broadly. This is an excellent opportunity to collaborate with the portfolio managers, asset managers, and analysts for the general account (through the CIO’s organization) and MassMutual’s asset management subsidiaries; and enterprise technology (including data science) teams.
The Team
The Capital & Investment Risk Management team is responsible for the identification, measurement and analysis of MassMutual’s portfolio, credit and capital risks. The team recommends risk management strategies and equips senior leadership with information they need to take advantage of opportunities and mitigate risks. Members of the team bring expertise and experience across a range of risk measurement and management disciplines, focused continuous improvement and development and business acumen.
The Impact
- Lead the efforts to implement, develop and enhance ERM’s analytical capabilities related to credit/market risk across a wide range of fixed income asset classes.
- Building on MassMutual’s current approach, assist in developing and syndicating a comprehensive framework for measuring portfolio credit & market risk, that considers different accounting and capital regimes, including asset and liability impacts, with a particular emphasis on economic capital.
- Lead complex initiatives including those are cross-functional with broader impact in Credit Risk and become a key participant and partner with those teams and stakeholders.
- Lead and mentor junior quantitative analysts
- Automate and expand the use of Moody’s credit risk tools in place today and build risk- reward optimization.
- Use of Python/ SQL. Also, use of spreadsheets and VBA to do prototyping and analyze data.
- Lead to strengthen ERM’s use and development of tools and analytics to support portfolio credit/ investment risk and derivatives counterparty risk.
- Expand use of economic scenario generators to support market, credit and capital risk analysis, and stress and scenario testing.
- You will scope and implement modeling, including building out requirements where not yet fully defined or understood. You will be agile, accountable and resilient in driving results.
- Liaise and collaborate with business stakeholders, quantitative model developers, technology.
The Minimum Qualifications
- Minimum 7 years of relevant work experience in investment (credit/market) quantitative risk analytics
- Experience in leading projects in quantitative analytics capacity.
- Moderate to high level skills in Python and SQL and development skills
- Strong quantitative model development & implementation skills and ability to validate analytical results
- Experience in quantitative risk modeling across a wide range of asset classes and relevant product knowledge in an investment setting.
- Bachelor’s and/or Advanced degree in a quantitative discipline
- Desire to use your quantitative and programming skills in a hands-on setting to deliver new functionality.
The Ideal Qualifications
- 7-10 years of relevant work experience is desirable.
- Master’s degree or PhD in Computer Science, Financial Engineering, Mathematics, Physics, engineering or similar quantitative discipline is preferred
- Knowledge and experience working with derivatives and hedging risk management
- Experience in using Moody’s Analytics credit risk tools is desirable
- Previous experience working on liability driven investing projects within an insurance company is desirable
- Experience applying machine learning techniques in the financial industry is desirable.
- Experience in implementation & migration quantitative models to cloud-based platform
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