Your primary responsibilities will include:

• Preparing performance attribution reports for senior management and portfolio managers, highlighting primary PnL drivers and providing ad-hoc deep-dive analysis as required.
• Investigating and explaining material PnL moves on a Trade Date and T+1 basis, acting as a key point of contact for traders, risk, and finance on all PnL-related queries.
• Developing systematic controls to validate and enhance PnL attribution processes, including automated reconciliations, threshold-based alerts, and exception reporting.

In addition, you will be responsible for monitoring and validating real-time and end-of-day pricing for all fixed income instruments across Rates, Credit, and FX, including derivatives and structured products.

You will also maintain a strong working knowledge of Greeks-based risk sensitivities and their application to PnL attribution across fixed income derivatives, collaborate with quants and risk teams to ensure risk factor decompositions used in PnL attribution are accurate and aligned with the firm's pricing and risk models, and support the testing and validation of new pricing models and their impact on PnL and risk reporting.

To succeed in this role, you will need to have an advanced degree in a quantitative discipline such as Engineering, Mathematics, Physics, Financial Engineering, or a related field, experience in PnL attribution, derivatives pricing/valuations, quantitative risk, or a closely related function within a front-office, risk, or portfolio analytics environment, and knowledge of fixed income products and their risk profiles across Rates, Credit, and FX, including derivatives, structured products, and asset-backed securities.

You will also need to have solid coding skills in Python, with the ability to work efficiently with large datasets, build automation, and develop analytical tools, and excellent communication skills, with the ability to interact effectively with portfolio managers, quants, risk, and technology teams across the firm.

If you are a collaborative team player with a strong willingness to support others, adapt quickly, and thrive in a fast-moving, high-pressure environment, we encourage you to apply for this exciting opportunity.

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You will collaborate closely with technical teams to support the development and refinement of new AI capabilities, with a primary focus on quantitative financial risk management domains. Your expertise will drive the selection and rigorous resolution of complex risk-related problems, including market risk modeling, credit and counterparty risk, liquidity and funding risk, operational and model risk, stress testing & scenario analysis, Value at Risk (VaR)/Expected Shortfall (ES), risk attribution, capital allocation (economic/regulatory), and enterprise-wide risk frameworks under regulatory regimes (Basel, Dodd-Frank, IFRS 9, etc.).

This role requires exceptional quantitative rigor, rapid adaptation to evolving guidelines, and the ability to deliver precise, technically sound critiques, derivations, and solutions in a fast-paced environment. As a Finance Risk Expert, you will directly support xAI's mission by helping train and refine frontier AI models. You will teach the models how risk professionals quantify uncertainties, model tail events, assess portfolio vulnerabilities, ensure regulatory compliance, perform stress testing, and make data-driven decisions to protect capital and maintain financial stability.

Your tasks may include recording audio walkthroughs of risk models, participating in video-based scenario reasoning, or producing detailed quantitative risk analysis traces. All outputs are considered work-for-hire and owned by xAI.

Responsibilities:

• Use proprietary annotation and evaluation software to deliver accurate labels, rankings, critiques, and comprehensive solutions on assigned projects
• Consistently produce high-quality, curated data that adheres to strict quantitative and regulatory standards
• Collaborate with engineers and researchers to develop and iterate on new training tasks, risk-specific benchmarks, and evaluation frameworks
• Provide constructive feedback to improve the efficiency, precision, and usability of annotation and data-collection tools
• Select and solve challenging problems from financial risk domains where you have deep expertise

Basic Qualifications:

• Master’s or PhD in a quantitative discipline: Quantitative Finance, Financial Engineering, Financial Mathematics, Statistics, Applied Mathematics, Econometrics, Risk Management, Operations Research, Physics, Computer Science (with risk/finance focus), or closely related field or equivalent professional experience as a quantitative risk analyst, risk modeler, or risk quant
• Excellent written and verbal English communication (technical reports, regulatory documentation, explanatory breakdowns)
• Strong familiarity with financial risk data sources and platforms (Bloomberg, Refinitiv, Moody’s Analytics, S&P Capital IQ, RiskMetrics, internal bank risk systems, regulatory filings, Basel/FRB datasets, etc.)
• Exceptional analytical reasoning, attention to detail, and ability to exercise sound judgment with incomplete or ambiguous data

Preferred Skills and Experience:

• Professional experience in quantitative risk management, model development/validation, or risk analytics at a bank, hedge fund, asset manager, insurance company, regulator, or consulting firm
• Track record of publication(s) or contributions in refereed journals/conferences on risk, econometrics, statistics, or quantitative finance
• Prior teaching, mentoring, or training experience (university, industry workshops, regulatory training)
• Proficiency in Python/R for risk modeling (pandas, NumPy, SciPy, statsmodels, QuantLib, PyTorch/TensorFlow for ML risk models, etc.) and familiarity with risk systems (Murex, Calypso, Numerix, etc.)
• Experience with Monte Carlo simulation, copula models, stochastic processes, time-series analysis, extreme value theory, or machine learning for risk (anomaly detection, credit scoring, etc.)
• Knowledge of regulatory capital frameworks (Basel III/IV, FRB CCAR, SR 11-7 model risk guidance, IFRS 9/CECL, Solvency II)
• CFA, FRM, PRM, CQF, or similar risk-focused certifications
• Previous exposure to large language models, AI safety, or quantitative evaluation pipelines

Location and Other Expectations:

• Tutor roles may be offered as full-time, part-time, or contractor positions, depending on role needs and candidate fit
• For contractor positions, hours will vary widely based on project scope and contractor availability, with no fixed commitments required
• Tutor roles may be performed remotely from any location worldwide, subject to legal eligibility, time-zone compatibility, and role specific needs
• For US based candidates, please note we are unable to hire in the states of Wyoming and Illinois at this time
• We are unable to provide visa sponsorship
• For those who will be working from a personal device, your computer must meet xAI’s minimum hardware requirements

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