Quantitative researcher

The platform will not only implement connectivity and price distribution logic but will also include sophisticated decision making mechanism based on complex data analysis and solutions of optimization problems.

Electronic trading is a business associated with huge amounts of data such as market data and trade data from a variety of sources. In the presence of thousands of clients and tens of liquidity providers the natural goal of our team is to generate profits via optimal pricing and hedging strategies. We believe that there is no better way to made optimal decisions rather than use the historical data analysis. To implement this analysis, we are looking for a quantitative researcher who will contribute to the system from the point of view of business logic and optimal decision making. Since the goal of the team is to compete with the best liquidity providers in the market our quantitative algorithms should use the best practices from the industry or use in-house innovations. The workflow of the quantitative research has two main streams: the invention of new components of the system and estimation of the optimal parameters of existing components. Thus, we expect that QR is familiar with machine learning, econometrics techniques as well as stochastic control problems. Ideas proposed by QR and parameters estimated will be directly incorporated into production system and impact financial results of the department and the company as a whole.

Requirements:

• Ability to autonomously write code in Python.
• Knowledge of base packages: numpy, scipy, sklearn, pandas etc.;
• Knowledge of econometrics;
• Knowledge of machine learning;
• Basic knowledge of stochastic control;

Responsibilities:

• Work on wide scope of data science tasks: Estimation of price sensitivity of clients;
• Estimation of market impact;
• Estimation of information leakage;
• Price prediction;
• Solution of the position management problem;
• Generate new ideas on the optimization of the trading logic;

Conditions:

• Flexible working hours;
• Competitive performance-based compensation;