Quantitative Advisory Services
The Quantitative Advisory Services works with clients in areas such as market risk and credit risk, providing financial services with regulatory and risk modelling challenges. Some of the major duties are data analysis to identify patterns. Development of statistical models to forecast items like credit loss, examination, and model validation to ensure the precision of outputs. Translating technical methodologies and results for non-technical audiences into digestible knowledge.
RUDDERLY MENTORSHIP PROGRAM
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KEY SKILLS
Graduating with a BS or MS degree in a STEM field
Superior quantitative and logical reasoning skills
Interest in strategic games and/or competitive activities
Experience with programming or scripting in Python is a plus
Assist in the identification, evaluation, selection, and monitoring of existing and new investments
Attend/present at investment committee meetings on PE related recommendations, as needed
Maintain reports to track and monitor prospective and existing investments, including performance attribution
Provide support for various portfolio operational matters, including accounting, risk management, and other governance-related tasks
Conduct special projects (e.g., analysis of private equity holdings, portfolio exposures, performance attribution, portfolio construction, investment strategy, industry trends and office technology initiatives)
Produce written reports and presentations, as needed
Represent the institution to the investment community, including attending investment manager meetings and industry conferences
PROS
Good payscale
A lot of career opportunities
Employment growth
A lot of learning
CONS
No particular work ethics
Employees are alienated
No job security
OPPORTUNITY TYPES
GOT WHAT IT TAKES?
Government
Companies
Good communication skills
Good logical reasoning skills
Good analytical skills
Teamwork
KEY OPPORTUNITIES
Books
Podcasts
Networking Groups
Interesting Facts about the career
Quantitative data makes measuring various parameters controllable due to the ease of mathematical derivations they come with. Quantitative data is usually collected for statistical analysis using surveys, polls or questionnaires sent across to a specific section of a population. The retrieved results can be established across a population.