Quant Researcher

Quant Researcher

Location: Remote
Compensation: Performance Based
Reports To: CIO / Portfolio Manager

About Dark Alpha Capital

Dark Alpha Capital is an emerging investment firm currently operating as a proprietary trading group, deploying internal capital across systematic, quantitative, and event-driven strategies. Our near-term objective is to build a multi-year, independently audited track record grounded in repeatable process, robust risk controls, and scalable infrastructure.

Once performance is proven and validated, we plan to raise outside capital and transition into a fully structured hedge fund. Early hires will help build the research engine, tooling, and operating discipline that become the foundation of the future platform.

Role Overview

The Quant Researcher is responsible for generating, validating, and refining systematic trading insights that can be deployed in live trading. You will work closely with the CIO and Portfolio Managers to develop signal hypotheses, test them rigorously, and translate research into strategies with clear assumptions, defensible statistics, and defined risk frameworks.

This role is performance-linked. Upside is tied to strategies that are actually deployed and generate realized results.

Key Responsibilities

Signal Research and Strategy Design

• Source, propose, and prototype systematic signals across equities, rates, FX, commodities, volatility, or cross-asset frameworks.
• Develop factor models, regime models, and statistical arbitrage concepts with explicit assumptions and guardrails.
• Define the “why” behind signals, including economic rationale and failure modes.

Backtesting and Validation

• Build and maintain rigorous backtests, including realistic assumptions on execution, costs, slippage, borrow, and constraints.
• Perform walk-forward testing, stability checks, sensitivity analysis, and scenario stress testing.
• Apply robust statistical validation methods (e.g., out-of-sample testing, multiple-testing controls, bootstrap/Monte Carlo where appropriate).

Risk, Portfolio Construction, and Monitoring

• Evaluate strategy risk exposures (factor, sector, rates, vol, liquidity) and correlation behavior across regimes.
• Propose sizing frameworks, hedging overlays, and drawdown controls aligned with pod-level risk limits.
• Monitor live strategies post-deployment, diagnose performance drift, and recommend corrective actions.

Research Engineering and Documentation

• Produce clean, reproducible research workflows in Python, with structured experiment tracking and version control.
• Maintain a research library of signals, datasets, model versions, and decision logs.
• Deliver clear research write-ups and concise performance summaries for PM/CIO review.

Collaboration and Deployment Support

• Partner with Quant Developers and Execution Traders to translate research into production-ready models and monitoring.
• Support deployment readiness by specifying inputs, logic, risk rules, and expected behavior under stress.
• Participate in pod discussions and contribute to continuous improvement of the research process.

Qualifications

• Bachelor’s degree in a quantitative discipline (Mathematics, Statistics, Computer Science, Physics, Engineering, or similar). Advanced degree is a plus.
• Strong Python proficiency and ability to produce reproducible research (data handling, modeling, testing, and reporting).
• Solid grounding in statistics, probability, econometrics, and time-series analysis.
• Experience designing and evaluating systematic strategies, including rigorous backtesting and out-of-sample validation.
• Understanding of market microstructure concepts and how they impact tradability, slippage, and strategy robustness.
• 2+ years in quantitative research, systematic trading, risk research, or closely related experience.
• High accountability and comfort operating in an early-stage environment where outputs must be deployable, not theoretical.

Operating Model and Expectations

• Remote-first, execution-driven environment. Communication is direct and outcomes-focused.
• Research must be defensible, documented, and deployable under realistic constraints.
• Compensation is explicitly linked to deployed strategies and realized results, consistent with the firm’s performance-based model.