Machine Learning Engineer- Multi Asset Risk Systems (MARS)

The Multi Asset Risk Systems team (MARS) provides risk calculations and analytics across various asset classes by applying distributed computing techniques that span hundreds of machines. In addition to expanding our main product offering, our main concern is performance which requires using different methods and technologies to achieve the optimal solution. We use big data software, distributed computing algorithms, dynamic resource allocations, and cluster management among others.

We compute billions of derivatives valuations daily to provide quality data and risk analytics to our clients. Derivatives valuation is a challenging process, requiring the evaluation of nonlinear and discontinuous equations with high-dimensional input data and multidimensional output. We often use numerical techniques like Monte Carlo simulation and differential equation solvers to achieve the valuations. Our growing team is responsible for developing customized machine-learning pipelines to model all aspects of derivatives valuation to speed up our platform.

What's in it for you:

You will join a close-knit team of 120 engineers and growing. You will have the opportunity to use machine-learning tools like Gaussian processes, nonlinear regression, gradient boosting, and neural networks. We use these techniques to model aspects of derivatives valuation including:

• The stochastic evolution of high-dimensional market data
• The multi-dimensional relationship between derivatives values and the underlying contract parameters and market data
• Derivatives lifecycle events

We'll trust you to:

• Understand the dynamics of the underlying financial systems being modeled
• Develop computationally inexpensive and interpretable model pipelines using combinations of new, customized models and out-of-the-box models
• Write production-quality infrastructure software to gather data, automate model training, and run the models at a large scale

You'll need to have:

• BA, BS, MS, PhD in Computer Science, Engineering or related technology field
• 3 plus years of post-grad industry software development experience covering the full software development life-cycle
• Working knowledge of Python, C++, or Java
• An advanced understanding of statistical modeling techniques, including machine-learning frameworks
• An understanding of financial derivatives

We'd love to see:

• Experience in distributed systems, including distributed database and computation software