AlgoQuant Asset Management
Dubai (preferred)
- London
- New York – Reports to Head of Research – Rolling start
About AlgoQuant
AlgoQuant Asset Management is a multi-strategy digital asset manager allocating capital across
25+ internal and external quantitative trading pods. Founded in 2018, we have evolved into an
institutional platform combining trading edge with strong governance and advanced technology,
serving family offices and institutional investors globally.
The role
We are hiring a Senior Quant Researcher with deep machine learning and deep learning expertise
to drive the next generation of alpha research at AlgoQuant. This is a senior, high-ownership role
for someone who has moved beyond applying ML frameworks — you understand why models
work, where they break, and how to turn raw predictive signal into live, capital-weighted strategy.
You will lead research into complex, non-linear signal generation across digital asset markets,
working across spot, derivatives, and on-chain data. You will own research end-to-end: from
problem formulation and data architecture through to live deployment and performance attribution.
You will also set the standard for rigour and methodology across the research team.
Responsibilities
- Design and deploy advanced ML and DL models for alpha signal generation across digital
asset markets
- Work across the full model stack: feature engineering, architecture selection, training and
validation regimes, and live signal monitoring
- Apply and adapt state-of-the-art techniques — transformer architectures, graph neural
networks, reinforcement learning, and ensemble methods — to financial prediction
problems
- Build robust, production-grade research pipelines with a rigorous approach to preventing
lookahead bias, data leakage, and overfitting
- Analyse microstructure, order flow, and cross-venue dynamics to enrich feature sets and
improve signal quality
- Collaborate with engineers to move models from research to production infrastructure
- Mentor junior researchers and raise the bar for statistical rigour across the team
- Contribute to shared research infrastructure, tooling, and datasets
What we are looking for
- Exceptional quantitative background — PhD or equivalent research depth in machine
learning, statistics, physics, mathematics, or computer science
- Genuine expertise in modern ML and DL: transformers, attention mechanisms, graph
neural networks, boosting algorithms (XGBoost, LightGBM), and reinforcement learning —
not just familiarity, but hands-on implementation experience
- A track record of applying ML in a live, capital-at-risk environment — attributable P&L or
measurable out-of-sample performance from systematic strategies
- Rigorous, almost paranoid approach to model validation — deeply experienced with the
failure modes of ML in finance: overfitting, regime change, feature leakage, and
non-stationarity
- Strong programming skills — Python required; C++ or Rust a strong plus for production
performance
- Experience working with large, complex, or unconventional datasets; on-chain data
experience a plus
- Self-directed and high-agency — you set your own research agenda and drive it to
completion
- Crypto market exposure a strong plus; intellectual curiosity about digital asset market
structure essential
