Data Scientist

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Reinforcement Learning (RL) Python TensorFlow PyTorch OpenAI Gym 5+ years experience Data Scientist We are seeking an experienced Data Scientist with a specialization in Reinforcement Learning (RL) and its application in quantitative trading or portfolio management. The selected candidate will work on developing a package for RL to apply to many use cases and end their contract training other data scientists in how to utilize the package.

Key Responsibilities:

Package Development: Develop a package to execute Reinforcement Learning (RL) on AWS Sagemaker. The package should allow for the use of multiple agents and allow for easy implementation of agents across different problems. Needs the ability to deploy multiple agents to solve problems mainly for portfolio management purposes.

• Deploy the building blocks of a reinforcement learning problem to data packages.
• Code should be modular. Strong acumen for OOP preferred.
• Package should address complex multi-agent problems which are common in portfolio optimization.
• Package should have functionality for the evaluation of models, hyper-parameter optimization, model/scenario explainability, and the display of results to the trading team.
• Experience in Quantitative trading or deploying RL for similar optimization problems required.

Proof of Concept Deployment: Design, implement, and evaluate the RL package on a POC after package deployment.

Documentation & Training: Train other data scientists to swap agents, change the environment, rewards, etc... Detailed documentation of the RL package will be required.

Qualifications:

Education: Master's or PhD in Computer Science, Machine Learning, Financial Engineering, Operations Research or a related field.

Experience: At least 5 years of professional experience in reinforcement learning deploying models at scale on AWS required. Experience contributing to building python packages in RL or other ML fields preferred. Prior model deployments with a specific focus on applications in quantitative trading or portfolio management preferred.

Technical Skills: Proficiency in Python and familiarity with popular RL libraries such as TensorFlow, PyTorch, and OpenAI Gym. Must also have a familiarity with cloud platforms deploying and training RL models (AWS Sagemaker).

Software Engineering Skills: Solid grasp of software design principles, data structures, and algorithms pertinent to trading environments.

Communication: Excellent written and verbal communication skills; ability to relay complex trading strategies and outcomes to various stakeholders.

Teamwork: Demonstrated experience working effectively in cross-functional teams, especially within a high-pressure trading context.

Nice-to-Have:

Experience with high-frequency trading environments.

Publications or notable contributions in the realm of RL for quantitative finance.