* Python library developer.
* Creating R libraries to be used as interface between model R code and generic data ...
- Python library developer.
- Creating R libraries to be used as interface between model R code and generic data retrieval API.
- Python and Pyspark package development.
- Communicate and direct model development teams on implementation of statistical models.
- Write specifications and documentation for R interfaces.
- Interface with Quant users and gather feedback on delivered analytical data.
- Communicate needed changes to development team.
- Work with QA team on test plan reviews and assist in QA testing process with requirements clarifications and questions.
Functional Responsibilities: Specific tasks include but are not limited to:
- Work on one of following initiatives within Global Markets Data Analytics group and other projects.
- Liaise with Dev team on defining data structures used in computing modeling.
- Liaise with QA team on explaining requirements and assisting with QA testing process.
- Support UAT process.
Required Skills: Technical / Analytical Skills:
- Experience with advanced R coding including package development.
- R code optimization and memory management.
- Understanding of core R data structures required.
- Understanding of Python and PySpark development.
- Good understanding of test that, dplyr and sparklyr packages.
- Software development background preferred and experience working in Agile environment.
- Comfortable working with UNIX/LINUX environment.
- Understanding of GIT and/or SVN processes.
- Previous experience with RESTful API framework.
- Good understanding of XML and JSON.
- Good comfort level with relational databases and SQL.
- Experience creating interface specification documents, attribute mapping documents, functional specifications.
- Proficiency with MS Excel, MS Word, MS Visio and MS PowerPoint.
Nice to have:
- Experience with Docker, Jenkins, Azure and Openshift.
- Experience with open-source big data technologies (Hadoop, Hive, Impala, MLLIB, Oozie etc.) for large scale data analysis.
- Experience with visualization tools like Tableau.
- Experience with RShiny.
- Knowledge of regression techniques for modeling the relationship between an output variable and several input variables.
- Understanding of regulatory guidelines (CCAR, Basel) around Model Risk Management.
- Working knowledge of various Fixed Income and Equities products, for example Derivatives, FX and Cash Equities.
- Strong verbal and written communication skills
- Strong analytical and problem solving skills
WORK EXPERIENCE/BACKGROUND: Experience/Background
- 7-8 years working experience
- provided by Dice