AI Algorithm Researcher

AI Algorithm Research

Job Overview Conduct cutting-edge research in AI algorithms specifically designed for financial applications. You'll develop novel approaches to portfolio optimization, risk modeling, and market prediction while publishing research that positions our firm as a thought leader in quantitative AI for asset management.

Key Responsibilities

•Research and develop novel machine learning algorithms for financial time series prediction and portfolio optimization 

•Design advanced neural network architectures for multi-modal financial data (text, numerical, graphical)

•Investigate reinforcement learning approaches for dynamic portfolio rebalancing and risk management 

•Collaborate with academic institutions and publish research in top-tier AI and finance conferences

•Prototype innovative algorithms and conduct rigorous backtesting using historical market data

•Work with engineering teams to transition research concepts to production systems

•Analyze alternative data sources and develop signal extraction methodologies using deep learning

Required Qualifications

•PhD in Computer Science, Machine Learning, Statistics, Mathematics, or related quantitative field

•Strong research track record with publications in AI/ML conferences (NeurIPS, ICML, ICLR) or finance journals

•Deep expertise in machine learning theory, optimization, and statistical modeling 

•Proficiency in Python and mathematical computing (NumPy, SciPy, JAX)

•Experience with deep learning frameworks and implementing novel architectures from research papers

•Strong mathematical foundation in linear algebra, calculus, probability, and statistics

•Knowledge of financial mathematics, econometrics, or quantitative finance preferred

Preferred Qualifications

•Post-doctoral research experience or senior research scientist position at leading AI lab

•Previous collaboration with financial institutions or fintech companies

•Expertise in specialized areas: time series analysis, graph neural networks, causal inference, or Bayesian methods

•Experience with high-performance computing and distributed training of large models

•Track record of transitioning research to production applications

•Understanding of market microstructure, derivatives pricing, or portfolio theory

Technical Skills

•Research: PyTorch, JAX, mathematical optimization libraries, statistical software 

•Mathematics: Advanced linear algebra, stochastic processes, information theory

•Programming: Python, R, C++, MATLAB, GPU computing (CUDA)

•Specialized: Time series analysis, graph neural networks, Bayesian inference

•Financial: Portfolio optimization, risk modeling, derivatives mathematics

•Tools: Jupyter, research computing clusters, experiment tracking (Weights & Biases)