11 ene
Mas Global Consulting
Nuevo Casas Grandes
Our customer, a Top Fortune 50 company and a global leader in financial services, located in Northern California, has asked us to assemble a winning team to enhance their internal offerings based on Artificial Intelligence, Machine Learning, and best-in-class innovation practices to maximize customers' investments.As a backend ML engineer, you will have the opportunity to work alongside financial lab engineers, best-in-class financial innovation experts, researchers, product managers, and stakeholder teams to create the next generation of a security and asset management platform.
You will be implementing the methods being developed by the researchers,
in production-grade code and building surrounding infrastructure such that the analytics may be accessed via a variety of access patterns (API, files, etc.
)**Technical Skills**- A bachelor, master, or Ph.D. in computer science or similar software engineering focused major 5+ years of professional experience implementing machine learning models, optimizers, and other quantitative tooling in a professional setting.- Ability to read and understand mathematical documentation in the form of whitepapers containing the methods being developed.- Proficiency in Python in the contexts of automation, infrastructure development, and quantitative computing.- Strong understanding of Python scientific libraries such as Numpy, Scipy, Pandas, PyTorch, etc.- Understanding of testing in the context of software engineering, including unit testing, integration testing, performance testing, etc.- Excellent communication and business/leadership skills- Software Architecture- Ideally past data science background- A second major, minor, or degree in an adjacent quantitative discipline, such as math, engineering,
or physics is a big plus.- Background in finance will be looked upon favorably but is not a requirement.- Oanda- Financial libraries**Main Functions**- Accurately implement methods described in technical documentation developed by researchers.- Develop accompanying infrastructure to make the models and analytics developed accessible via a variety of access patterns.- Test all software being developed for accuracy and stability.
**Must Have**- Understand mathematical documentation in the form of whitepapers.- Phyton (scientific libraries such as Numpy, Scipy, Pandas, PyTorch, etc.
)- Understanding of testing in the context of software engineering**Location**: Anywhere in the world - ideally same time zone to Palo Alto, CA8doeap0Yw8
Muestra tus habilidades a la empresa, rellenar el formulario y deja un toque personal en la carta, ayudará el reclutador en la elección del candidato.