Quantitative Strategist - Equity Execution

Quantitative Strategist – Equity Execution

Financial Firm

NYC – Downtown

Hybrid, onsite 3 days/ week

150-160K base +bonus

Job Summary:

Large Broker Dealer which supports various types of Algorithmic trading for institutional clients is seeking a Quantitative Strategist to join their team responsible for creating equity execution strategies. In this role you will Research, Design and Test Analytical Models to enhance Algorithmic Trading performance. You will work closely with business stakeholders to conduct rigorous back-testing of proposed trading signals for optimal market liquidity. You will write detailed specifications for new algorithmic features and perform UAT. Your work will support Block Trading, Algorithmic Trading, Program Trading, Dark Pool Execution services and execution analytics, as well as Execution Algorithms like VWAP, TWAP, POV and Pre and Post Trade Analytics. Hybrid role, onsite 3 days per week in Downtown NYC office.

Must have:

PhD or MS in Math or Similar field

Experience creating equity execution algorithms

Python (preferred), R or similar

Role Overview:

As a Quantitative Strategist you will play a key role in optimizing and advancing our equity execution strategies by researching, designing, and testing sophisticated analytical models to enhance algorithmic trading performance. Collaborating closely with business and technology stakeholders, you'll conduct rigorous back-testing of proposed trading signals to ensure they align with company’s mission of providing institutional investors with seamless, high-quality liquidity. Your work will involve writing detailed specifications for new algorithmic features and performing user acceptance testing to ensure the robustness of our solutions. Additionally, you will lead post-production analysis to assess new models and their impact, ensuring that the company’s cutting-edge technology consistently delivers optimal results for our global client base.

This hybrid role requires a solid foundation in quantitative analysis, a deep understanding of equity execution algorithms, and proficiency in programming languages such as Python. A passion for innovation and an ability to apply research-driven solutions to real-world trading challenges will make you a strong fit within our collaborative, forward-thinking environment.

Job Duties:

Work independently and with key stakeholders across the EQS business to research, design and test analytical models to improve algo performance.

Conduct back-testing of proposed trading signals and analytics to ensure suitability and performance.

Write specifications of new algorithm features for implementation by the Technology teams.

Perform user acceptance testing of the new models and algorithm features.

Work with others within EQS to produce post-production statistical analysis of new models and comparison versus existing models.

Experience and Competencies:

Essential

Master’s degree or PhD in a technical field such as Mathematics, Computer Science, Statistics, Engineering, Machine Learning, or similar, or equivalent years of relevant experience preferred.

Proficiency in a programming language for numerical analysis: Python (preferred), R or similar.

Experience with machine learning as demonstrated by at least one significant project, which could from school, work, or your own research.

The ability to produce clear, concise and re-runnable analysis.

Extremely detailed and concise

Desired:

Experience with SQL, Git, Jupyter notebooks.

Knowledge of equity trading systems and their architecture.

The FIX messaging protocol.

Able to consider pragmatic solutions to real-world client trading requests.

Willing to work autonomously when required.

Must be interested in participating in new projects and helping other individuals and departments within the company

Knowledge of equity execution algorithms, strategies and equities market micro-structure.