Quantitative Researcher Machine Learning Jobs in Florida

About the Firm

We are a US-based systematic macro trading firm focused on global futures and FX markets. Our approach combines quantitative research, statistical modelling, and machine learning to develop and manage predictive trading strategies across multiple time horizons.

We operate a lean, research-driven structure with strong institutional backing and a collaborative team of experienced portfolio managers and quantitative developers. The environment is fast-moving, low-bureaucracy, and highly focused on research quality and real-world trading impact.

The Role

We are hiring a Quantitative Researcher to strengthen and extend our core research capability in signal discovery, statistical validation, and predictive modelling.

You will work directly with portfolio managers and developers to improve how we generate, evaluate, and deploy trading signals. The focus is not just building models — but ensuring they are statistically robust, economically meaningful, and genuinely predictive out-of-sample .

This is a hands-on research role covering the full lifecycle from idea generation to live strategy impact.

Key Responsibilities

1. Research and develop predictive signals across futures and FX markets
2. Design and implement rigorous time-series validation frameworks
3. Apply statistical methods to distinguish true signal from noise and overfitting
4. Build and evaluate machine learning models for forecasting and classification
5. Work with large-scale financial datasets and engineered features
6. Collaborate with developers to translate validated research into production systems
7. Continuously improve research methodology and experimental design
8. Review and assess new ML/statistical techniques for practical trading relevance

What We’re Looking For

We are open to different backgrounds, but strong candidates will demonstrate depth in statistics, time-series modelling, and applied machine learning .

Statistical & Research Strength

1. Strong understanding of time-series data and non-i.i.d. processes
2. Deep knowledge of statistical inference, hypothesis testing, and overfitting risks
3. Experience evaluating predictive models in noisy real-world environments
4. Ability to rigorously assess whether a result is statistically and economically valid

Machine Learning & Modelling

1. Strong experience with classical ML methods (regularised regression, tree-based models, ensembles)
2. Practical understanding of model selection, bias-variance trade-offs, and feature engineering
3. Experience with Python ML stack (NumPy, pandas/polars, scikit-learn, etc.)
4. Exposure to deep learning (e.g. transformers or sequence models) is a plus, but not required

Engineering & Data

1. Comfortable working with large, messy datasets in Python
2. Experience building or contributing to research/backtesting pipelines
3. Familiarity with reproducible research and experiment tracking

Preferred Background

We expect strength in at least one of the following:

1. Quantitative research in systematic hedge funds or prop trading firms
2. ML / statistical research applied to real-world or time-series data problems
3. PhD (or equivalent experience) in Mathematics, Statistics, Physics, Computer Science, or related field
4. Experience working with financial or other complex sequential datasets

What Makes This Role Different

1. Direct impact on live trading strategies
2. Lean, high-ownership environment with minimal bureaucracy
3. Strong focus on research rigor over model complexity
4. Close collaboration with portfolio managers and developers
5. Opportunity to shape how systematic research is conducted within the firm

Nice to Have (Not Required)

1. Experience with futures, FX, or macroeconomic datasets
2. Exposure to causal inference or econometric modelling
3. Experience with distributed computing or large-scale model training
4. Interest in experimental or automated research frameworks (e.g. multi-agent systems)

Why Join Us

This is an opportunity to join a growing systematic macro firm at an early stage, where research quality directly drives performance