AO Investment Risk Analyst

Summary

AllianceBernstein (AB) is hiring two Investment Risk Analysts to join our global buy-side Investment Risk function. You will partner with senior Investment Risk Managers, Portfolio Managers, and investment teams to measure, monitor, and explain risk across global, multi-asset portfolios. This role requires strong collaboration with senior risk managers and a clear drive to learn the investment business at a sophisticated global asset manager. You will contribute to impactful work with senior investment personnel to manage and mitigate investment risk.

This role is designed for someone who is highly capable with technology and quantitative thinking and wants to develop into a risk manager over time. In the first 6–12 months, your primary focus will be building and maintaining risk reports, improving data pipelines and controls, and running day-to-day surveillance to identify unusual moves in exposures and risk.

This is not a software engineering role, but you must be able to write clean, reliable analytical code (Python + SQL), work with large datasets, and communicate findings clearly in an investment context.

Responsibilities

• Risk reporting and production analytics (primary in year 1)
• Build, enhance, and maintain recurring risk reports and dashboards for multi-asset portfolios (e.g., factor exposures, volatility/correlation, beta’s, stress/scenario results, concentration, drawdowns, tail indicators).
• Improve production robustness: data quality checks, controls, logging, monitoring, and documentation for repeatable daily/weekly workflows.
• Implement scalable analytics patterns (modular code, reusable functions, configuration-driven reporting) to support new strategies and PM requests.
• Assist with validation activities (back-testing, sensitivity analysis, benchmarking) and document assumptions/limitations clearly.
• Risk surveillance and exception monitoring
• Perform day-to-day surveillance across portfolios to identify and triage material changes in exposures, leverage, liquidity proxies, concentration, factor tilts, and tail risk.
• Investigate drivers of changes (market moves vs. positioning vs. model/input changes) and escalate notable issues with clear evidence.
• Maintain “run-the-business” playbooks: what to check, how to diagnose, and how to communicate.
• Quantitative risk analysis
• Conduct bespoke analytical work on strategies, skill measurement, position sizing, and hedging — including backtests and sensitivity analyses.
• Research risk premia, market structure, and market-dynamics effects on portfolio construction and strategy performance.
• Analyze derivatives strategies and structured products to quantify payoff characteristics, tail exposures, and liquidity/convexity risks.
• Support model validation by benchmarking results, stress-testing assumptions, and documenting limitations.
• PM engagement and risk communication
• Translate model output into investment language: what changed, why it matters, and what’s likely noise vs. signal.
• Produce crisp written summaries and visuals to support portfolio conversations and decision-making.
• Provide constructive, evidence-based challenge to portfolio managers and investment teams.
• Over time, contribute to mitigation and optimization discussions (hedging, concentration reduction, risk-budgeting).
• Support client-facing staff with clear, actionable information about portfolio risk and risk-management practices.
• Market awareness and risk culture
• Stay current on market developments and risk events impacting covered strategies.
• Contribute to a strong risk culture: disciplined analysis, clear communication, and thoughtful escalation.
  
  

• 3-10 years of relevant experience, preferably at an asset management or financial services firm
  
  

• Mathematics / Statistics / Physics
• Engineering (e.g., Computer, Electrical, Industrial, Mechanical)
• Computer Science / Data Science / Information Systems
• Economics / Econometrics / Finance / Quantitative Finance
• Or other fields with strong quantitative and programming content
  
  

• Python (strong): can write clean, maintainable analytical code; comfortable with pandas/numpy and structured workflows for repeatable reporting.
• SQL (strong): joins, aggregations, window functions; comfortable reasoning about performance and data correctness.
• Generative AI: Ability to use generative AI tools prudently to amplify productivity, augment analyses, and translate model outputs into clear investment-language summaries. Experience with MCP and development API’s for LLM’s a plus.
• Quantitative foundation: probability/statistics, time series intuition, analytical problem solving.
• Data discipline: careful handling of missing data, outliers, stale inputs; ability to implement checks/controls and explain data limitations.
• Communication: can explain results clearly to a mixed audience; strong writing and comfort presenting concise findings.
• Team orientation: responsive, accountable, and comfortable working with global stakeholders across time zones.
• Portfolio Risk Concepts: Exposure to portfolio/risk concepts (at least some subset of multi factor models, duration/convexity, spread risk, FX risk, derivatives basics, VaR/ES, stress testing).
• Helpful but not required: Familiarity with financial risk tools such as Bloomberg PORT, MSCI BARRA, Omega Point, FactSet, Axioma, SimCorp