Quantitative Finance Analyst (Closed)
SkillStorm is seeking a Quantitative Finance Analyst for our client in Chicago, IL. Candidates must be able to work on SkillStorm's W2; not a C2C position. EOE, including disability/vets.
- Client has an opportunity for a Quantitative Finance Analyst within the GRALIB team as part of Alternative Modelling Group & Quantitative Solutions (AMG-QS) , a team within Global Risk Analytics (GRA).
- Global Risk Analytics (GRA) is a sub line of business within Global Risk Management (GRM). The GRA team provides quantitative capabilities supporting global risk management and capital management and develops a consistent set of risk and capital models and analytical tools that drive the company’s technology infrastructure.
- The GRALIB team is a part of the Global Risk Analytics (GRA) organization. It is responsible for maintaining and expanding a shared software library focused on statistical methods and machine learning. GRALIB team members do not develop new models or algorithms, nor do they work as data scientists. Rather, they develop and maintain reusable tools that are used by both modelers and data scientists who work on other GRA teams.
Required and Desired Candidate Qualifications Including # of Years Prior Experience Needed:
- At least intermediate-level knowledge of Python, with substantial practical experience in this area.
- At least two years of experience developing and maintaining a reusable software library or some other component in a complex software system.
- Understanding of basic principles of large-scale software development, best practices of programming and code maintenance, and systems design.
- Working knowledge of common numeric algorithms and data structures.
- Basic familiarity with object-oriented design and functional programming.
- Prior work experience with econometrics, including both linear regression and MLE principles and techniques. However, we shall also consider candidates who lack such experience, but have very strong mathematical background otherwise.
- Solid understanding of basic topics in statistics is highly desirable.
- Practical experience with using Apache Spark is highly desirable.
- If the candidate has never used Spark, he/she should at least have prior exposure to other types of large-scale distributed computing.