Job Title: Quantitative Researcher (VP) - Execution Algorithms (Equities) and Electronic Trading
Location: New York
Department: Equities & Futures Quantitative Research
Job Type: Vice President (VP)
Department: Equities & Futures Quantitative Research
Job Type: Vice President (VP)
About:
An opportunity within a Tier 1 US Investment bank to join a global team specializing in all aspects of quantitative research for Equities, Futures, and Options execution strategies. The team is responsible for developing and optimizing agency algorithmic strategies, smart order routing (SOR), trading analytics, portfolio construction, risk management, inventory management, and internalization. The group also supports the electronic Futures & Options execution business, combining advanced quantitative techniques with cutting-edge trading technologies to provide innovative solutions and insights.
Position Overview:
We are seeking a highly skilled and motivated Quantitative Researcher with a strong background in developing execution trading algorithms (both agency and principal). The ideal candidate will possess a deep understanding of trading dynamics, quantitative modeling, and algorithmic strategy development, with expertise in both equity and futures markets. The candidate will also have experience working on a Client-Related Business (CRB) desk, where they will have been responsible for delivering tailored solutions and strategies to clients, optimizing trading execution, and improving overall trading performance. A strong foundation in statistical analysis, machine learning, and optimization techniques will be crucial to success in this role.
Key Responsibilities:
- Execution Algorithm Development: Design, develop, and optimize execution trading algorithms (agency and principal) for equities, futures, and options markets, with a strong focus on improving client execution performance.
- Signals Research & Analysis: Conduct research to identify market signals that can enhance trading strategies and improve execution outcomes for clients.
- Modeling & Strategy Development: Build and implement advanced quantitative models to guide trading decisions, including single-position and portfolio schedule optimization.
- Market Microstructure Analysis: Analyze market microstructure in equities and futures markets, understanding order placement methodologies, routing, market impact, and transaction cost analysis.
- Client-Facing Interaction (CRB Desk Experience): Collaborate with client-facing teams to develop and implement execution strategies tailored to specific client needs. Provide quantitative analysis and strategic insights to enhance client trading performance and achieve optimal execution.
- Risk Management: Develop and implement quantitative models to assess and manage trading risk and portfolio risk for both internal and client-facing executions.
- Back-Testing & Performance Evaluation: Conduct rigorous back-testing and evaluation of execution algorithms and trading strategies to assess their effectiveness and profitability across various market conditions.
- Machine Learning & Advanced Techniques: Apply machine learning techniques, including classification, reinforcement learning, and dynamic programming, to improve trading strategies and models.
- Collaboration & Knowledge Sharing: Work closely with other members of the quantitative research team, developers, traders, and risk managers to implement and refine trading strategies and algorithms.
Qualifications:
- Education: Master's or Ph.D. in a quantitative field such as Mathematics, Physics, Computer Science, Financial Engineering, or Statistics.
- Experience: Proven experience in quantitative research or algorithmic trading, with a focus on execution algorithms (agency or principal). (0r) Previous experience on a CRB (Client-Related Business) desk, delivering execution solutions for clients, is desirable.
- Technical Skills:
- Strong proficiency in programming languages such as Python, C++, KDB, or Java.
- In-depth understanding of quantitative methods, including statistical analysis, time series analysis, regression, and model calibration.
- Familiarity with optimization techniques, including linear/non-linear programming, stochastic optimization, and dynamic programming.
- Experience with transaction cost analysis, market impact models, and order routing methodologies.
- Experience with machine learning techniques, including classification, reinforcement learning, and time-series forecasting, is a plus.
- Knowledge Areas:
- Market microstructure and order placement methodologies in equities and futures markets.
- Risk management models and portfolio construction techniques.
- Smart order routing (SOR) and transaction cost analysis.
- Client-Facing Skills: Strong experience in a client-facing role, especially in providing tailored execution strategies and quantitative solutions to optimize client trading outcomes.
- Soft Skills: Strong problem-solving, analytical, and communication skills. Ability to work in a fast-paced and collaborative team environment.
Desirable Attributes:
- Familiarity with large-scale data sets and high-frequency trading environments.
- Experience in back-testing algorithms and optimizing execution strategies for different market conditions.
- Understanding of regulatory environments impacting algorithmic trading and client-facing strategies.
