What you’ll be doing

• Transform legacy code into robust, high-quality Python code that complies with internal coding standards and includes comprehensive test coverage
• Work with the machine learning engineering team to develop and implement robust testing strategies, including unit, integration, and end-to-end tests
• Build a maintain scalable machine learning pipelines on AWS cloud platforms using SageMaker and Snowflake
• Assist in deploying machine learning models into production environments, ensuring scalability, reliability, and performance
• Maintain and monitor existing pipelines and model APIs to ensure optimal functionality
• Work closely with data scientists, machine learning engineers, DevOps and InfoSec teams to initiate and manage various releases
• Translate business requirements into technical solutions through effective communication
• Translate technical project details into clear documentation and intuitive diagrams that help integrate information flows with upstream and down stream teams

Requirements

• Graduate degree in a quantitative discipline (or equivalent experience) or will be completed within three months.
• Proficiency in Python and SQL development.
• Experience with modern software development practices, including version control systems and infrastructure-as-code.
• At least one year of relevant work experience is required
• Experience in building and deploying machine learning models in cloud environments (AWS, Snowflake)
• Desired:
• "Highest degree specifically in: Statistics, Mathematics, Physics, Engineering, Computer Science, Economics / Econometrics, Finance, Data Science, Machine Learning, AI, Financial Engineering, Computational Finance, Operational Research, Industrial Engineering. "
• Active AWS Solutions Architect Associate certification
• Experience with SAS (statistical analysis) programming language
• Experience working with API frameworks (Flask, FastAPI) and Amazon SageMaker
• Ability to design and implement comprehensive testing strategies.