Our client, a leading global multi $ Bn hedge fund are now looking to hire a very talented trading quantitative researcher to join any one of their global offices. Positions are available in London, NYC, and Geneva.
This team is responsible for designing, implementing & continuously evolving trading research toolkit – transaction costs analysis, measurement, attribution, benchmark comparison through a normalized and standardized framework, dashboards and reports.
The successful candidate must possess knowledge of markets structure across multiple asset types; experience working with tick and order book level pricing data; understanding of advanced statistical techniques, market impact, limit order models, time series analysis, coding experience with columnar / time series databases and SQL.
The Trading Research team is part of the systematic technology group that are responsible for designing, implementing and evolving systems required to enable the quantitative investment process.
- Design, develop and evolve build transaction costs analysis (TCA) framework
- Build execution performance measurement and attribution framework, dashboards and reports
- Work with pricing & reference data sets such as tick and order book level pricing data to conduct execution performance analysis
- Apply knowledge of advanced statistical techniques, market impact, limit order models to work with columnar databases to conduct time series analysis
- Collaborate with other systematic leadership and systematic trading teams to translate their needs into scalable, standardized solutions
- Conduct statistical analysis over large datasets
- Develop and maintain engineering best practices including focus on high standards across all stages of SDLC
- 5+ years of Python (or R), Java (or C++), SQL experience
- Significant experience working with Python scientific computing packages (numpy, scipy, pandas, matplotlib, sklearn, etc.)
- Knowledge of reference data, pricing data, across multiple assets types (credit, rates, currencies, derivatives, equities)
- Knowledge and experience building execution algorithms
- Experience with columnar databases (e.g. KDB)
- Strong quantitative reasoning skills and an interest in working at the intersection of research and software engineering
- MS/ PhD in Computer Science, Financial Engineering, or related discipline
- Self-motivated, proactive
- Ability to perform well under pressure
- Enthusiastic, flexible and adaptable
- Ability to work effectively and independently as well as part of a team
- Good interpersonal skills
- Strong communication, problem solving skills
- Understanding of market impact & limit orders models
- Understanding of broker execution algorithm
Please get in touch or send your resume to:
email@example.com to discuss further.