Director, Electronic Trading Products
About the position Position Description: Drives the improvements of systematic trading algorithms to realize low market impact, low cost, and ensure the best execution. Optimizes the implicit and explicit cost of trading decisions based on periodic performance monitoring and review. Merges systematic trading and analytics products and creates platforms that clients can use to navigate and execute in an increasingly complex market. Leads the creation of Cloud platforms to provide real-time and historical Data-as-a-Service (DaaS) to customers. Maintains and ensures the integrity of Transaction Cost Analysis (TCA) database and intelligent modules. Shapes next-generation tools and statistical analysis techniques to improve performance against clients’ trading objectives. Responsibilities • Works closely with clients to enhance their trading strategies, educate them on market structure and trading tools, and provide expert consultations to address their unique challenge. • Oversees multi-asset class research and product development across domestic and international equities, and options. • Demonstrates key performance indicators (KPIs) related to revenue and expense, driving the success of both trading strategies and client satisfaction. • Works with product developers, traders, clients, and software developers to deploy electronic trading products and financial services. • Investigates market signals, including price and volume prediction models. • Creates and supports analytical systems used for comprehensive post-trade performance measurement and attribution. Requirements • Bachelor’s degree in Financial Mathematics, Applied Mathematics, Financial Engineering, Computer Science, Engineering, or a closely related field (or foreign education equivalent) and five (5) years of experience as a Director, Electronic Trading Products (or closely related occupation) building systems and solutions for financial investment or trading decisions, using R, Python, PL/SQL databases, and Artificial Intelligence (AI) and Machine Learning (ML) techniques. • Or, alternatively, Master’s degree in Financial Mathematics, Applied Mathematics, Financial Engineering, Computer Science, Engineering, or a closely related field (or foreign education equivalent) and three (3) years of experience as a Director, Electronic Trading Products (or closely related occupation) building systems and solutions for financial investment or trading decisions, using R, Python, PL/SQL databases, and Artificial Intelligence (AI) and Machine Learning (ML) techniques. • Demonstrated Expertise (“DE”) performing data analytics to improve trading products on Electronic Trading Platforms in a capital markets environments with a Broker Dealer or a Trading Technology Provider, on Regulation National Market System (Reg NMS) equity securities, using KDB+/Q and Python. • DE building quantitative models on trading data, including regression-based market impact and prediction models employing supervised and unsupervised optimization strategies, using Python or Q; modernizing and digitalizing platform products over Amazon Web Services (AWS) for electronic trading experience; reviewing order flow patterns on analytics platform, to tailor custom strategies and reduce trading costs for clients; and conducting A/B tests for hyperparameter tuning and functional enhancements in codebase. • DE working in KDB+ environment to transform and enrich structured and unstructured data for TCA and building purpose-built time-series databases for hypothesis-driven research for algorithmic trading; querying in Q for vectorized data wrangling with large datasets; generating advanced data visualizations and insight using Tableau and computational and plotting libraries in Python; and performing repository management and automation within a Linux environment, using GitHub. • DE providing actionable recommendations for optimizing liquidity access from exchanges, dark pools, dealers, and private rooms through trading algorithms and smart order routers (SOR); and assessing quality and quantity of liquidity provided in trading venues by evaluating normalized performance factors, including risk metrics, toxicity, and counterparty presence, using TCA Database with Python, R, or Q.