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
