AVP - Quant Analytics

We are looking for a highly talented individuals who are working on the research and development of analytics, signals and trading tools used in the context of cash equities trading businesses. The work covers a number of product lines including pre-trade, intra-trade, post-trade products, electronic execution and central risk platforms across all major global markets.

A successful candidate will have a demonstrated ability in building quantitative analytics tools with preference given to those with direct experience in market impact cost modeling, algo/signals research or automated management of portfolio risk and a working knowledge of equites market microstructure (or other asset class). The candidate will use their knowledge of statistics to design, implement and validate models. They will use their strong programming capability to collect, manipulate and aggregate large data sets and develop production ready components. They will use their presentation skills to summarize model outcomes and write up compelling analytical notes for internal desks and external clients.

Data types that the team works with include ultra-high frequency market trade and quote (TAQ) data and electronic trading order and execution data.

This data is used to develop:

- Algorithmic trading analytics

- Microstructure/signals research

- Pre-trade cost and risk estimates

- Portfolio risk optimizer

- Quantitative research for internal and external publications

- Transaction Cost Analysis (TCA) and improvement of strategy performance

- Bespoke quantitative research for EE clients

- The employee will be responsible for the accuracy and completeness of all modelling and analysis

Qualifications:

Education:

- Bachelor's degree in Computer Science/Electrical Engineering/Mathematics/Statistics from a premier academic institution

Experience:

- 6-12 years of industry experience

Skills Required:

- Strong analytical and quantitative background, with good knowledge in one or more of the following disciplines - probability, quantitative finance, computer science, linear algebra, numerical methods, statistics, econometrics, time series analysis

- Strong programming and computer science skills

- Experience with Q/KDB+/Python is mandate.