AI-Driven Quantitative Research Analyst

Risk Professionals, Inc.

New York (NY)

On-site

USD 90,000 - 125,000

Full time

14 days+
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Job summary

The Office of Risk Analysis (ORS) within the Division of Examinations seeks a Quantitative Research Analyst (Financial Engineer) in New York, NY. This role applies advanced mathematics, statistics and data science to monitor securities laws violations and support EXAMS.

You will work with modern languages like Python, R, C#, C++, and q/kdb+ on large datasets, communicate complex concepts to non-technical audiences, and help develop methods to detect fraudulent or disruptive trading.

Qualifications

  • Degree in Mathematics, Statistics, or Actuarial Science required at baccalaureate level.
  • Combination of education and experience acceptable per the posting.
  • At least one year of specialized experience equivalent to GS/SK-13.
  • Experience applying quantitative research to financial data; AI/ML; coding in Python/R/C/C++/C#/kdb+/Linux.

Responsibilities

  • Lead development and application of text analytics, AI, LLMs, RAGs, and Fine-Tuning.
  • Work in Python, R, C#/C++, q/kdb+ or functional languages on analytics projects.
  • Explain technical concepts to non-technical audiences in an examination context.
  • Research analysis of investment returns, risk and derivatives using math and coding.
  • Develop modeling to identify manipulative or disruptive trading; support examinations.

Skills

Python
R
C#
C++
q/kdb+
Text analytics
AI/ML
LLMs
RAGs
Fine-Tuning

Education

Mathematics / Statistics / Actuarial Science

Tools

q/kdb+
Linux
Cloud environments
Python

Job description

The Office of Risk Analysis (ORS) within the Division of Examinations seeks a Quantitative Research Analyst (Financial Engineer) in New York, NY. This role applies advanced mathematics, statistics and data science to monitor securities laws violations and support EXAMS.

You will work with modern languages like Python, R, C#, C++, and q/kdb+ on large datasets, communicate complex concepts to non-technical audiences, and help develop methods to detect fraudulent or disruptive trading.

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