About the Company
We are a systematic investment manager seeking talented and driven individuals to join us at the ground floor. It’s an opportunity for maximum ownership, accelerated impact, and career trajectory:
- Maximum Impact: You won't just run experiments in isolation; your research will directly shape the strategies we trade. Every iteration of your work influences every investment decision we make.
- Significant Resources and Stability: Despite being a startup, we are well-capitalized and backed by well-known, highly respected Allocators. This provides the stability, scale, and significant resources necessary to invest aggressively in top-tier technology and talent from the outset.
- Zero Bureaucracy: Work in an agile, flat structure where good ideas move to production fast. Decisions are made quickly and your technical opinion carries immense weight.
- Upside: In addition to competitive compensation, this role offers a significant opportunity for long-term profit-sharing, aligning your success directly with the growth of the fund.
About the Role
Quantitative Researcher
Deep Research · Machine Learning · Systematic Trading
Overview
We are seeking an exceptional Quantitative Researcher to join our systematic trading team. Our group has a strong quantitative foundation, with experienced portfolio managers and developers who build and trade systematic strategies across Futures and Foreign Exchange (FX) markets. We are adding a dedicated researcher operating at the frontier of machine learning and advanced statistics to deepen our quantitative capabilities and push the team’s modeling and statistical toolkit forward.
You will embed within a collaborative team that provides rich market context and a well-established research infrastructure. Your role is to elevate the team’s research: bringing additional statistical rigor, introducing modern ML techniques where they genuinely add value, and helping distinguish signal from noise. You will also be expected to bring your own ideas to the table and work with the team to assess their applicability.
Key Responsibilities
- Statistical Evaluation: Add depth to the team’s evaluation of trading ideas and signals. Apply advanced statistical methods to distinguish genuine predictive content from artifacts of noise and overfitting.
- Model Development: Build, train, and validate predictive models across a range of approaches, selecting the right tool for each problem.
- Signal Discovery & Feature Engineering: Identify and extract predictive signals from large-scale data sets. Design feature engineering approaches grounded in statistical reasoning.
- Validation: Design and implement rigorous validation frameworks that guard against overfitting and data-snooping bias.
- Research-to-Production Collaboration: Work alongside quantitative developers to translate validated research into production-grade systems.
- Frontier Methods: Survey developments from top ML and statistics research venues and assess their practical applicability.
- Agentic Research: Explore frameworks that leverage clusters of AI agents as part of the research process, helping automate and scale experimentation while identifying where human judgment remains essential.
Required Qualifications
Research & Analytical Skills
- Experience: 5+ years of professional experience in quantitative research, or an equivalent track record in a top-tier academic or industrial research lab with demonstrated application to real-world data problems.
- Statistical Depth: Expert-level command of statistical inference, hypothesis testing, time-series econometrics, and the pitfalls of multiple testing. You should bring a level of statistical sophistication that meaningfully raises the bar for the team.
- Research Judgment: A track record of knowing when a result is real and when it isn’t. Demonstrated ability to evaluate complex quantitative claims, identify methodological weaknesses, and propose better approaches.
Machine Learning & Computational Skills
- Core Modeling Mastery: Deep expertise in penalized and sparse linear methods, gradient-boosted trees, random forests, and ensemble techniques. We expect more than practitioner-level familiarity—you should have a thorough understanding of the underlying theory and the conditions under which these methods succeed or fail.
- Deep Learning & Neural Networks: Hands-on experience with modern deep learning architectures (e.g., transformers, recurrent networks, probabilistic and generative models). We are looking for someone who can identify where neural approaches genuinely add value over classical methods.
- Programming: Experience in Python and its scientific/ML ecosystem, including familiarity with common frameworks for deep learning (e.g., PyTorch, JAX, TensorFlow) and data analysis (e.g., NumPy, Pandas/Polars, Scikit-learn).
- Computational Scale: Experience training models on large data sets using GPU clusters and distributed computing.
- Time-Series & Real-Tim