As part of GreySpark’s Data Practice, you will be part of a specialised team of data professionals focused on a broad range of data analysis and data science projects and initiatives. The Data Practice assists capital markets clients to address key data challenges. These can range from electronic trading driven data analysis projects to data modelling for specific ad-hoc client projects. GreySpark promotes industry best practices and adds value by working collaboratively with our clients to provide deliverables bespoke to their needs.
To excel at this role, you should be detail-oriented, objective and be able to articulate technical details succinctly and clearly. We are looking for demonstrable experience of working on a wide variety of data science projects in a commercial or academic environment, knowledge of data best practices and keen interest of staying abreast of the latest technological developments that are relevant.
At GreySpark Partners, you are empowered to take ownership of your work and intellectual development and to build a network that will further your career ambitions. We offer a clearly defined career path and apply a meritocratic process in promoting the next tranche of leaders in our growing firm. You will have the opportunity to work in a team-focused, dynamic business that recognises success.
- Statistical analysis of financial data (client/product/trade datasets)
- Working with business analysts to define and gather data requirements
- Sourcing and joining data from multiple sources
- Automation of routine analysis and regulatory reporting requirements
- Communicating technical results to a non-technical audience
- Preparing documentation to explain data transformation steps
- Ad-hoc data souring/analysis as and when the client requests
- Understanding of relational database management tools, in particular; SQL, Kdb+
- Advanced knowledge of Microsoft Excel (basic knowledge of VBA is a bonus)
- Data manipulation skills; cleansing, enriching and linking datasets
- Understanding of data best practices and basic data governance principles
- Demonstrable interest in data analysis e.g. participation in online competitions, independent research etc.
Technical skills (optional):
- Working knowledge of Python libraries such as matplotlib, pandas and numpy would be advantageous
- Experience with data visualisation tools Qlikview, Tableau, matplotlib
- Experience with Agile technologies (JIRA) and version control software (Git / Subversion)
- Familiarity with big data tools such as Cassandra, Hadoop (Pig, Hive, Impala), Spark, MongoDB
- University degree in computer science, mathematics, engineering or other quantitative subjects