This role pays more than
87%
of similar roles. Most pay
$151,737–$213,878
— the shaded band above.
At the midpoint, this role pays about
$238k
versus about
$183k
for comparable roles.
Based on 239 similar postings.
Employer
About Balyasny Asset Management
Balyasny Asset Management (BAM) is a global multi-manager hedge fund offering diversified investment strategies across equities, macro, commodities, and systematic trading.
Balyasny Asset Management currently has
59 open roles
on FindRole.
Listed pay typically runs
$175,000–$250,000
across 30 roles with salary data.
The US FTR, Gas and Power Risk, Quantitative Researcher joins the Commodities Risk Management team to partner with risk and investment teams in building trading, risk, and physical commodity models. The role involves formulating and implementing models for risk analysis of commodity products and derivatives, including term structures and volatility surfaces. Key responsibilities include improving risk reporting tools, performing P&L attribution, conducting stress testing using statistical metrics, and calibrating risk systems. The position focuses on the specific domain of US FTR, Gas, and Power markets, requiring experience in modeling physical assets like gas storage and power tolls. Candidates must possess a master's or doctorate in quantitative fields and demonstrate proficiency in Python, SQL, and libraries such as pandas and numpy. The role addresses complex risk topics including market liquidity, liquidation costs, and seasonality within commodity risk models.
London, United Kingdom+278 days ago
$200,000–$225,000
C++
Python
SQL
NoSQL
Machine Learning
Time Series Analysis
Stochastic Modeling
Optimization
Numerical Methods
Linear Algebra
Statistics
Probability
Simulation
Data Analysis
Workflow Orchestration
DAG-based Systems
Distributed Data Workflows
Statistics
Python
R
Machine Learning
Predictive Modeling
Regression Analysis
Multivariate Analysis
Data Visualization
Tableau
Power BI
Big Data
Business Intelligence
Statistical Process Control
Data Cleansing
Experimental Design
Data Integrity Management