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 a variety of 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:

• 10+years of relevant experience in a quantitative finance field, with roles such as a quant researcher / quant developer / quant trader in a major bank or hedge fund

• Strong academic background with an advanced degree (Masters or Doctorate) in a quantitative discipline such as Math, Physics, Computer Science, Financial Engineering

• 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