Job Description:
As a Quantitative Software Engineer on the Enterprise Risk Analytics team, your main responsibilities include:
Apply quantitative methods to develop capabilities that meet line of business, risk management and regulatory requirements
Work effectively within and across Agile teams to design, develop, test, implement, and support high-quality software solutions across a full stack of development tools, and technologies
Explain complex technical concepts to both technical and non-technical stakeholders
Strong commitment to continuous learning, staying up to date with technologies, and best practices in software engineering
Have a creative and innovative mindset, able to think outside the box to solve complex problems
Pay attention to detail by carefully reviewing code and documentation to identify and fix issues to protect the quality and reliability of software
Be adaptable, able to work in a dynamic environment, and adjust to changing requirements and shifting priorities
Required Skills:
5+ years of software design and development experience using Python, PySpark/Hadoop, Pandas, NumPy, SciPy, Jupyter notebook etc.
Experience with full Software Development Life Cycle (SDLC) including DevOps using Bitbucket/Git, Jenkins, SonarQube etc.
Understanding of data engineering best practices related to architecture patterns supporting varying data types, volume, and velocity
Analytical skills – Ability to troubleshoot and logically assess problems and determine solutions
Good problem-solving skills, understanding of different data structures, algorithms, and their usage in solving business problems
A broad knowledge financial markets, products, and risk management
Degree in Computer Science, or other related fields (MS or PhD is a plus)
Shift:
1st shift (United States of America)
Hours Per Week:
40
