Principal Quant

• 1) QUANTITATIVE PRODUCT OWNERSHIP • Devise and own a highly consistent, coherent, and principled quantitative direction across trading, margining, liquidation, lending, and risk-related products. • Ensure that individual models, parameters, and mechanisms fit together into a harmonious system, rather than a collection of locally-correct but globally-fragile designs. • Own the end-to-end correctness, feasibility, and desirability of quantitative products in production. • Take ownership over preventing tricky edge cases, stress scenarios, and failure modes from hitting production. • Act as the first line of defense for user, partner, and internal feedback related to quantitative behavior: • answering questions about correctness and intent • diagnosing whether feedback reflects misunderstanding, edge cases, or real design flaws • driving fixes or adjustments when models do not behave as intended • Treat post-launch behavior as a continuation of product design, continuously refining models based on observed outcomes and feedback. Biasing strongly towards system consistency during revisions, and avoiding repeated fragile patches. • 2) CROSS-FUNCTIONAL LEADERSHIP & EXECUTION • Act as the product owner for quantitative surfaces that are inherently difficult for others to reason about. • Write exceptionally clear, precise specifications that engineers can implement correctly and safely. • Translate complex quantitative concepts into explanations appropriate for each audience: • simplified mental models for users and partners • implementation-level clarity for engineers • risk and trade-off framing for leadership • Own alignment-driving end-to-end: • proactively surface concerns, disagreements, and edge cases • address feedback thoughtfully rather than deferring or waiting for others • drive conversations to clear decisions and committed direction • Take direct responsibility for validating implementations of quantitative products: • design and execute deep testing in non-production and production environments • reason about edge cases, stress scenarios, and failure modes that others are unlikely to catch • use QA support where helpful, but remain personally accountable for correctness • Own the outcome when quantitative products are mis-implemented, even if gaps were not caught by QA, recognizing that the domain complexity requires quant-level validation. • 3) OPERATIONAL OWNERSHIP & RISK MANAGEMENT (PRIMARY, HIGH BAR) • Take first-class oncall responsibility for quantitative systems and risk-sensitive product behavior in production. • Act as a key responder during incidents involving: • abnormal trading behavior • liquidation anomalies • margin, risk, or insurance fund issues • extreme market conditions or tail events • Be accountable for real-time decision-making during high-stakes situations, including: • diagnosing root causes under pressure • advising on mitigations, parameter changes, or temporary safeguards • balancing user impact, platform safety, and long-term risk • Lead or co-lead post-incident analysis for quantitative failures, ensuring: • root causes are correctly understood (model vs implementation vs assumption) • durable fixes are made to models, parameters, or system design • learnings are fed back into product design and operational playbooks • Proactively identify latent systemic risks and work with engineering and risk teams to reduce them before they manifest as incidents. • Design quantitative products with operability in mind, including: • observability of key metrics and invariants • explainability of system behavior during abnormal events • safe failure modes and bounded blast radius