Senior Systematic Risk Manager - Balyasny Asset Management : Job Details

Senior Systematic Risk Manager We are looking for a Senior Risk Manager in New York to support our growing global Systematic Trading business. The individual will work closely with PM's across a range of strategies including, Equity Systematic, Macro Systematic, and Equity Arbitrage. The Senior Systematic Risk Manager will report to Co-heads of Systematic and Event Risk and be responsible for the following: Conduct daily and intraday analysis on the Systematic portfolios. Review process, architecture, simulation and backtest methodologies for Systematic portfolios. Refine the process of manager selection and performance assessment, with a keen focus on macro/thematic drivers and crowding analysis Develop methodologies and metrics for risk managing Systematic portfolios; build tools to monitor these and share with PMs. Contribute to BAM's risk analytics, processes and reporting both within the Systematic business and elsewhere. Build relationships with systematic PMs both in US and globally. Contribute to the development of large-scale intraday trading analytics Provide input and participate in weekly Global Risk committee discussions; make recommendations to Investment Committee where appropriate. Advise on whether BAM is being sufficiently rewarded for the risks it takes. Requirements: Strong academic background with an advanced degree (Masters or Doctorate) in a quantitative discipline such as Math, Physics, Computer Science, Financial Engineering 10 or ideally more years relevant experience in the quantitative finance field, with roles such as risk analyst / quant researcher / quant developer / quant trader in a major bank or hedge fund Strong programming skills in Python and SQL Well-versed in equity systematic strategies and statistical arbitrage Experience with and knowledge of equity factor models Strong communication skills. The role involves constant dialogue with all parts of the organization Rigorous research and analytical skills. Creative, motivated, hard-working, and strong all-around interest in financial markets. Practical approach to problem solving. Attention to detail - takes ownership of projects, strong focus on data quality, correctness, and intuitiveness of output.Nice to have: Knowledge of execution algorithms Knowledge of market microstructure Knowledge of transaction cost modelling Knowledge of systematic macro strategies Familiar with KDB/q, bash scripting, linux workflow. High performance computing Applied machine learning / generative AI experience Convex optimization (single and multi-period) Predictive modeling / alpha signal generation