Principal Quant

Grvt; Hong Kong; On-site; HKD 1,800,000 - 3,200,000; Full time

Job summary

Grvt in Hong Kong seeks exceptional senior ICs who combine product leadership with deep quantitative skill. You will own outcomes end‑to‑end for trading strategies, market‑making models, and risk‑related products, driving cross‑functional alignment with engineering and risk teams.

You will validate implementations, test extensively in non‑production and production environments, and ensure system‑wide consistency and robustness under stress.

Responsibilities

• Quantitative Strategies Design and Implementation
• Devise and own a highly consistent, coherent, and principled quantitative strategy across trading, market‑making, and alpha generation.
• Ensure that individual trading algorithms, market‑making models, and execution parameters fit together into a harmonious system designed for global efficiency.
• Own the end-to-end correctness, backtesting, and live performance of quantitative strategies in production.
• Take ownership over minimizing adverse selection, managing slippage, and optimizing execution quality under stress scenarios.
• Act as the primary quantitative expert for evaluating market behavior and strategy outcomes: analyzing realized volatility versus model predictions, diagnosing strategy performance drift and optimizing alpha signals, driving model adjustments based on market‑making effectiveness and P&L.
• Treat live trading results as the ultimate validation of design, continuously refining strategies based on real-world market dynamics. Biasing towards statistical robustness and long‑term scalability.
• 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.
• Cross‑Functional Leadership & Execution
• Act as the technical lead for the research pipelines and infrastructure required to scale GRVT’s trading capabilities.
• Write exceptionally optimized and clean code (Python/C++) for backtesting, research tools, and execution modules.
• Collaborate with engineering to bridge the gap between research and high‑performance production systems: designing low‑latency data pipelines for strategy inputs, optimizing the execution engine for market‑making responsiveness, building automated monitoring and attribution tools.
• 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.
• Risk Management & Live Trading
• Take full P&L responsibility for live trading strategies and systemic risk 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 risk monitoring during market volatility, 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, durable fixes are made to models, parameters, or system design, and 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 trading strategies with real‑time operability in mind, including observability of key metrics, explainability during abnormal events, and safe failure modes.

Job description

• Quantitative Strategies Design and Implementation
• Devise and own a highly consistent, coherent, and principled quantitative strategy across trading, market-making, and alpha generation.
• Ensure that individual trading algorithms, market-making models, and execution parameters fit together into a harmonious system designed for global efficiency.
• Own the end-to-end correctness, backtesting, and live performance of quantitative strategies in production.
• Take ownership over minimizing adverse selection, managing slippage, and optimizing execution quality under stress scenarios.
• Act as the primary quantitative expert for evaluating market behavior and strategy outcomes:
• analyzing realized volatility versus model predictions
• diagnosing strategy performance drift and optimizing alpha signals
• driving model adjustments based on market‑making effectiveness and P&L
• Treat live trading results as the ultimate validation of design, continuously refining strategies based on real-world market dynamics. Biasing towards statistical robustness and long-term scalability.
• 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.
• Cross-Functional Leadership & Execution
• Act as the technical lead for the research pipelines and infrastructure required to scale GRVT’s trading capabilities.
• Write exceptionally optimized and clean code (Python/C++) for backtesting, research tools, and execution modules.
• Collaborate with engineering to bridge the gap between research and high-performance production systems:
• designing low-latency data pipelines for strategy inputs
• optimizing the execution engine for market‑making responsiveness
• building automated monitoring and attribution tools
• 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.

What You’ll Do

• Quantitative Strategies Design and Implementation

• Devise and own a highly consistent, coherent, and principled quantitative strategy across trading, market‑making, and alpha generation.
• Ensure that individual trading algorithms, market‑making models, and execution parameters fit together into a harmonious system designed for global efficiency.
• Own the end-to-end correctness, backtesting, and live performance of quantitative strategies in production.
• Take ownership over minimizing adverse selection, managing slippage, and optimizing execution quality under stress scenarios.
• Act as the primary quantitative expert for evaluating market behavior and strategy outcomes:
• analyzing realized volatility versus model predictions
• diagnosing strategy performance drift and optimizing alpha signals
• driving model adjustments based on market‑making effectiveness and P&L
• Treat live trading results as the ultimate validation of design, continuously refining strategies based on real-world market dynamics. Biasing towards statistical robustness and long‑term scalability.
• 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.
• Cross‑Functional Leadership & Execution

• Act as the technical lead for the research pipelines and infrastructure required to scale GRVT’s trading capabilities.
• Write exceptionally optimized and clean code (Python/C++) for backtesting, research tools, and execution modules.
• Collaborate with engineering to bridge the gap between research and high‑performance production systems:
• designing low‑latency data pipelines for strategy inputs
• optimizing the execution engine for market‑making responsiveness
• building automated monitoring and attribution tools
• 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.

Risk Management & Live Trading

• Take full P&L responsibility for live trading strategies and systemic risk 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 risk monitoring during market volatility, 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 trading strategies with real‑time operability in mind, including:
• observability of key metrics and invariants
• explainability of system behavior during abnormal events
• safe failure modes and bounded blast radius

What We’re Looking For (Core Requirements)

We’re looking for exceptional senior ICs who combine strong product leadership with deep quantitative skill.

You should demonstrate:

• Prior experience building or operating trading venues, exchanges, or market infrastructure.
• A strong background in quant, with hands‑on experience in quantitative trading, or market‑making
• Proven strength as a Product Manager, including:
• owning outcomes end‑to‑end
• driving cross‑functional alignment
• writing high‑quality, precise specifications
• Deep understanding of margining, liquidation, leverage, and systemic risk mechanics.
• Strong operational mindset and comfort owning live P&L in production.
• Excellent communication skills, especially when explaining complex quantitative reasoning clearly.
• Sound judgment under ambiguity and high‑stakes decision‑making.