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AlgoQuant Asset Management - Head of Quantitative Research

Team: Trading

Reporting to: President / Co-Founder

At AlgoQuant, we're building the future of digital asset management; grounded in rigorous research, world-class technology and a relentless focus on performance.

We began as a proprietary trading firm, developing sophisticated algorithmic strategies and operating in some of the most complex and fast-moving markets. That DNA remains at our core, but today we are evolving into a fully remote, globally distributed investment management business. This transformation reflects a broader ambition: to scale our edge, deliver institutional-grade results, and set new standards for the industry.

Our quantitative environment is built to empower innovation, combining vast data capabilities, disciplined model development, and highly automated execution. Risk is embedded in every layer of our thinking, with robust measurement, control, and scenario analysis integrated into our systems and decision-making. Technology is not just a tool for us, it’s a core competency and a competitive advantage.

We’re seeking a dynamic Head of Quantitative Research to define and lead our quant research strategy. You will build, mentor, and inspire a high-calibre team of researchers and data scientists, oversee the full lifecycle of strategy development, and ensure seamless integration of research into our trading operations. This role demands a hands-on leader who champions next-generation technology, fosters deep collaboration across trading, and delivers a steady pipeline of alpha-generating ideas for Portfolio Managers to evaluate and deploy.

• Leadership & Vision
• Develop and execute the firm’s quantitative research roadmap, embedding next-generation technologies and AI at every stage.
• Recruit, mentor, and retain top quant talent, serving as the glue that binds research, trading, and engineering teams for maximal collaboration.
• Foster a culture of rigorous validation, creative experimentation, and continuous learning.
• Lead by example: ideate, prototype, and implement cutting-edge strategies using novel statistical, ML, or hybrid techniques.
• Build and maintain a robust pipeline of alpha signals, backtested, ranked, and packaged for PMs to review and select.
• Ensure reproducibility and high quality of code, research documentation, and deployment workflows.
• Cross-Functional Collaboration
• Act as principal liaison between research, trading, portfolio management, and risk, ensuring smooth hand-offs and shared ownership of strategy performance.
• Translate complex quantitative findings into actionable insights for stakeholders, using clear communication and collaborative workshops.
• Champion partner-driven innovation: work closely with data engineers, DevOps, and software teams to embed AI/ML solutions into production.
• Advocate and implement next-generation computing frameworks: cloud/distributed systems, GPU acceleration, and serverless ML platforms.
• Guide the evolution of research tools, data pipelines, and automated workflows, prioritizing scalability, security, and maintainability.
• Maintain deep awareness of AI/ML advances, ensuring the team rapidly adopts best-in-class libraries, techniques, and GenAI capabilities.

• Advanced degree (PhD/Master’s) in a quantitative field (Mathematics, Statistics, Physics, CS, Engineering).
• 8+ years in quantitative research at hedge funds, prop trading, or quantitative asset managers, with demonstrable leadership impact.
• Proven hands-on expertise in statistics, time series modeling, ML, deep learning, and generative AI for financial applications.
• Track record of using GenAI tools to accelerate research workflows, generate novel signals, or streamline code and documentation.
• Proficiency in Python (Pandas, NumPy, scikit-learn), plus experience in C++, Rust, or Java for performance-critical systems.
• Experience with large-scale data management (SQL/noSQL), high-performance/GPU computing, and cloud platforms (AWS/GCP).

• Prior leadership of quant teams delivering systematic alpha with robust risk controls.
• Expertise in alternative data sourcing, feature engineering, and on-chain analysis.
• Familiarity with portfolio optimization, execution algorithms, and real-time risk analytics.
• Contributions to open-source quant or ML libraries, or publications in top AI/finance venues.

Why Join AlgoQuant

• Lead research at a cutting-edge digital asset manager with institutional-scale ambitions.
• Shape the future of AI-driven trading by building and scaling GenAI-enhanced workflows.
• Competitive compensation, performance incentives, and equity in a high-growth environment.

A collaborative, fully remote culture that values innovation, autonomy, and rapid execution.

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