Senior Systematic Risk Manager

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