Quantitative Researcher

About Us

ArbWick is building an agentic investment platform for professional investors. We combine institutional-grade quantitative research, market data, deterministic analytics, and AI-driven workflows to help investment teams move from a market view to research, strategy construction, risk analysis, and execution.

We are a small, highly technical team working directly with professional investors and hedge funds. People joining ArbWick have significant ownership, work closely with the founders, and help shape both the research infrastructure and the product from an early stage.

The Team

The Quantitative Research team sits at the intersection of markets, research, engineering, and AI.

We develop the quantitative models, analytical frameworks, and research infrastructure that power ArbWick's institutional workflows. Our work spans systematic investing, derivatives, portfolio construction, market microstructure, risk, volatility, cross-asset research, and machine learning.

Researchers work across the full research lifecycle: idea generation, data sourcing, signal development, model implementation, backtesting, portfolio construction, validation, and production deployment.

You will also work closely with ArbWick's engineering and AI teams to transform quantitative research into reusable capabilities that can be invoked autonomously by AI agents and used across institutional investment workflows.

Responsibilities

• Conduct original quantitative research and develop systematic investment strategies across asset classes, including equities, digital assets, rates, commodities, credit, and FX.
• Generate, investigate, and test new investment ideas using financial intuition, statistical methods, machine learning, and large, diverse datasets.
• Research systematic strategies across directional, relative-value, cross-sectional, cross-asset, volatility, and derivatives-based approaches.
• Build and improve quantitative research infrastructure, including data pipelines, signal libraries, backtesting frameworks, portfolio analytics, risk models, and validation tools.
• Develop reusable quantitative functions and research capabilities that can be integrated into ArbWick's agentic investment workflows.
• Analyze strategy performance with particular attention to robustness, regime dependency, transaction costs, liquidity, market impact, capacity, and hidden risk exposures.
• Research opportunities across spot markets, equities, futures, perpetual futures, options, forwards, swaps, and other derivatives.
• Develop models for portfolio construction, position sizing, risk management, volatility, correlation, drawdowns, factor exposures, and scenario analysis.
• Work with large market and alternative datasets and determine which data is genuinely useful for producing robust investment signals.
• Design rigorous backtests and validation frameworks designed to identify overfitting, leakage, unstable relationships, and unrealistic implementation assumptions.
• Partner closely with the founders, engineers, AI researchers, and institutional clients to move ideas from research into production.
• Monitor live models and strategies and refine them as market structure, liquidity, and underlying relationships change.
• Help define how institutional-grade quantitative research should operate inside AI-driven investment systems.