I am working with a tier 1 multi-manager who are expanding their centralised machine learning functions.
Key Responsibilities:
- Research and develop machine learning models tailored to financial data and trading applications.
- Analyze large-scale, high-dimensional datasets to identify predictive signals and optimize trading strategies.
- Work closely with quantitative analysts, portfolio managers, and engineers to integrate ML models into live trading environments.
- Prototype, test, and validate algorithms to ensure robustness and scalability in production settings.
- Stay abreast of emerging machine learning techniques and evaluate their applicability to finance.
Preferred Qualifications:
- PhD or Master's degree in Computer Science, Machine Learning, Mathematics, Statistics, or a related quantitative field.
- Expertise in machine learning techniques such as supervised learning, reinforcement learning, deep learning, or unsupervised methods.
- Proficiency in Python, R, or C++ and ML frameworks like TensorFlow, PyTorch, or Scikit-learn.
- Familiarity with financial data (e.g., time series, tick data) and domain-specific challenges like low signal-to-noise ratios.
- Knowledge of optimization, probabilistic models, or Bayesian inference is a plus.
- 3+ years of experience in ML research or applications, preferably within a financial or quantitative context.
- Exceptional ability to solve complex problems with a structured, data-driven approach.
- Strong skills in conveying technical ideas to diverse audiences, including non-technical stakeholders.
If there is any interest, please apply directly or reach out to harry.moore(at)selbyjennings.com.
