below marketmost similar roles pay hereabove market
This role pays more than
82%
of similar roles. Most pay
$156,125–$215,185
— the blue band above.
At the midpoint, this role pays about
$228k
versus about
$186k
for comparable roles.
Based on 240 similar postings.
Employer
About Goldman Sachs
Goldman Sachs is a leading global investment banking, securities, and investment management firm providing financial services to corporations, financial institutions, governments, and individuals.
Goldman Sachs currently has
71 open roles
on FindRole.
Listed pay typically runs
$140,000–$250,000
across 34 roles with salary data.
JOB TITLE: GBM Public - Quantitative Researcher – Systematic Option Strategies – FICC Global Currencies and Emerging Markets Strats, VP – New York
As a Quantitative Researcher within the Global Currencies and Emerging Markets Strats team, you will drive applied research for Systematic Option Strategies. You will translate market observations into scalable systematic risk management and market-making strategies by developing forecasting models for volatility markets, including skew, smile, and term structure. Your daily work involves owning the end-to-end research process: idea generation, data processing, feature engineering, backtesting, and portfolio construction. You will design systematic hedging strategies for vega risks while accounting for transaction costs and liquidity. The role requires expertise in statistics, probability, time-series analysis, econometrics, machine learning, and optimization. You will utilize Python, Java, and large market datasets to solve complex problems in systematic FX options and volatility research.
Originate and test systematic option market making approaches using diverse techniques and signals.
Develop forecasting models for volatility markets including skew, smile, term structure, and market regimes.
Design and implement systematic hedging strategies for vega and related risks under real market constraints.
Own the end-to-end research process from idea generation and feature engineering to backtesting and performance attribution.
Apply robust research standards including bias controls, stress analysis, and sensitivity testing.
Assess production performance of deployed research and refine models as market conditions evolve.
Build reusable analytical tools and contribute to shared research infrastructure and data standards.
Communicate methods, assumptions, risks, and results clearly to trading desks and senior stakeholders.
What we're looking for
MS or PhD in statistics, mathematics, physics, computer science, engineering, operations research, econometrics, quantitative finance or a related quantitative discipline.
4–8 years of applied quantitative research experience in systematic options or volatility trading, systematic macro, derivatives, cross-asset relative value, statistical arbitrage or a closely related area.
Demonstrated experience developing and validating predictive models, signals, strategies or systematic hedging approaches.
Strong understanding of options, volatility and risk, including Greeks, volatility surfaces, carry and roll-down, P&L attribution, transaction costs and tail behavior.
Experience with options in FX, equities, rates, commodities or another liquid macro asset class.
Excellent foundation in statistics, probability, time-series analysis, econometrics, machine learning, optimization and experimental design.
Strong programming skills and experience working with large market datasets, research pipelines and backtesting frameworks; proficiency in Python, Java, or similar languages is beneficial (preferred).
Clear written and verbal communication skills and the ability to work effectively in a fast-paced collaborative environment.
Python
Java
Machine Learning
Artificial Intelligence
Big Data
Data Analytics
Portfolio Construction
Factor Analytics
FICC
Equity
Software Development
AI Tools