Description

The Office of Risk Analysis (ORS) within Division of Examinations is seeking a Quantitative Research Analyst (Financial Engineer), SK-1501-14 in New York, NY. The Quantitative Analytics Unit (QAU) within ORS is responsible for quantitatively driven analytical projects supporting EXAMS. The QAU applies mathematics, statistics, machine learning, AI and other modern data science methods to further the Division's monitoring and identification of securities laws violations.

Duties
In this role as a Quantitative Research Analyst, you will be responsible for:

Being at the forefront of the Division's development and application of text analytics, AI, LLMs, RAGs, Fine-Tuning;
Working in modern programming languages such as Python, R, C#/C++, q/kdb+ or functional languages on data analytics projects;
Communicating highly technical concepts and issues, including model parameters, to non-technical audiences in an examination context;
Researching areas such as the analysis of investment returns, risk and derivatives instruments, employing financial theory, applied mathematics, and hands-on coding;
Developing sophisticated modeling to help identify manipulative or disruptive trading; and
Support active examinations of registered entities for which advanced analytic methods are required.

Requirements

BASIC REQUIREMENT:

Degree: Mathematics, statistics, or actuarial science. The degree must be in a major field of study (at least at the baccalaureate level) that is appropriate for the position.
ORCombination of education and experience: Courses equivalent to a major field of study as shown in paragraph A above, plus additional education or appropriate experience.
MINIMUM QUALIFICATION REQUIREMENT: In addition to meeting the basic requirement, applicants must also meet the minimum qualification requirement
SK-14: Applicant must have at least one year of specialized experience equivalent to the GS/SK-13 level. Specialized experience includes the following:Applying the theories, principles, and processes of quantitative research to financial and securities industry data; AND
Utilizing AI, LLMs, machine learning; AND
Modeling and coding in languages such as Python, R, C/C++/C#, kdb+/q in Linux and cloud environment; AND
Manipulating large data sets to develop hands-on programmatic applications of probability and statistics.