FMG Risk Modeling Analyst

  • Not Specified
  • Gurgaon, Haryana, India
  • Permanent, Full time
  • BlackRock
  • 15 Oct 18

BlackRock helps investors build better financial futures. As a fiduciary to our clients, we provide the investment and technology solutions they need when planning for their most important goals. As of June 30, 2018, the firm managed approximately $6.3 trillion in assets on behalf of

BlackRock helps investors build better financial futures. As a fiduciary to our clients, we provide the investment and technology solutions they need when planning for their most important goals. As of June 30, 2018, the firm managed approximately $6.3 trillion in assets on behalf of investors worldwide. For additional information on BlackRock, please visit www.blackrock.com | Twitter: @blackrock | Blog: www.blackrockblog.com | LinkedIn: www.linkedin.com/company/blackrock.

Job Description:

Business Unit Overview:

The Financial Modeling Group (FMG) is responsible for the research and development of financial models underpinning the risk management and relative value analytics produced at BlackRock. The group also contributes to the infrastructure platform for the production of analytics and the delivery of analytic content to portfolio and risk management professionals both within and outside BlackRock. Given the diversity of business objectives among BlackRock Solutions clients and within BlackRock itself the models developed and supported by the Financial Modeling Group span a wide array of financial products, ranging from equity to fixed income to derivatives. In addition, members of FMG seek to provide analysis and insight on many different levels from analysis of the cash flows of a single bond to the overall financial risk associated with an entire portfolio, enterprise or balance sheet.

Job Description:

The successful candidate will lead the design and development of analytical models and methodologies used in investment process and portfolio risk management as well as maintain and support existing models and applications. We offer a dynamic, team-oriented environment with tremendous learning and growth potential.

Model Development:

  • Interacting with product managers and internal clients to specify and design new functionality
  • Empirical finance research using different econometric techniques
  • Implementation of solutions using R, Perl/Python, C++ and other software development tools
  • Deploying models into production ensuring that they are efficient and robust
  • Documentation and validation of models and processes

Model Maintenance:

  • Investigation and resolution of client issues under time constraints
  • Help to maintain and support our existing models and processes

Skills & Qualifications:

  • 4-5 years of experience in quantitative/statistical modeling
  • Advanced degree in a quantitative discipline – Master in Finance/Economics/Statistics/Financial Engineering/Math Finance, etc
  • Knowledge of investments, portfolio management, and financial econometrics and empirical asset pricing
  • Experience with analyzing data and various machine learning algorithms.
  • Excellent communication skills and ability to work well in a team environment
  • Prior experience with statistical software (e.g. SAS, R, S-PLUS, MATLAB) and strong background in programming
  • Exposure to scripting technologies like PERL/Python
  • Prior work experience in financial modeling (e.g. risk models, analytics) or data science is a strong plus

BlackRock is proud to be an Equal Opportunity and Affirmative Action Employer. We evaluate qualified applicants without regard to race, color, national origin, religion, sex, disability, veteran status, and other statuses protected by law.

BlackRock is proud to be an Equal Opportunity and Affirmative Action Employer. We evaluate qualified applicants without regard to race, color, national origin, religion, sex, sexual orientation, gender identity, disability, protected veteran status, and other statuses protected by law.

BlackRock will consider for employment qualified applicants with criminal histories in a manner consistent with the requirements of the law, including any applicable fair chance law.