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- Tasks: Design and develop models for alpha generation in trading and execution.
- Company: Join a leading firm in optimising trading strategies and internal liquidity solutions.
- Benefits: Enjoy a competitive salary, performance bonuses, and comprehensive benefits.
- Why this job: Be part of a collaborative team driving innovation in quantitative finance with real market impact.
- Qualifications: 5+ years in Quantitative Finance; PhD or Master's in Statistics required.
- Other info: Proficiency in Python and KDB is essential; remote work options may be available.
The predicted salary is between 96000 - 160000 £ per year.
The Central Liquidity Strategies (CLS) business manages a number of portfolios and products designed to optimize the firm’s trading and execution approach by providing internal liquidity solutions for portfolio managers on both a risk and agency basis. We are seeking an Alpha Researcher with experience in return/toxicity forecasting as it relates to market-making business offering pricing on larger blocks of equities either via outright risk pricing or other product structures.
Principal Responsibilities
- Modelling: Design and develop models to assist in alpha generation. Areas include: Automated evaluation of signal performance over time and feature engineering techniques to drive improvements. Combination of multiple signals to produce a single usable alpha for different contexts and attribution of performance. Robust estimation of key metrics such as signal correlations, decay, turnover and risk.
- Rigorous Grounding: Given inherent complexity and high dimensionality, employ methods to avoid overfitting and poor OOS performance based on sound statistical reasoning.
- Collaboration: Work with team members to decide the overall direction, design, and architecture of the platform, and collaborate with key stakeholders across the business.
Qualifications/Skills Required
- Required Experience: 5+ years of experience in Quantitative Finance setting, with a proven track record of developing robust alpha models, preferably in an Equities context.
- Education: PhD or Master’s degree in Statistics, or a related field with an excellent understanding of the theory behind statistical and machine learning methods.
- Technical Skills: Proficiency in Python and/or KDB, preferably both.
The estimated base salary range for this position is $160,000 to $250,000, which is specific to New York and may change in the future. Millennium pays a total compensation package which includes a base salary, discretionary performance bonus, and a comprehensive benefits package. When finalizing an offer, we take into consideration an individual’s experience level and the qualifications they bring to the role to formulate a competitive total compensation package.
Quantitative Researcher - Execution Services. employer: Millennium Management
Contact Detail:
Millennium Management Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Quantitative Researcher - Execution Services.
✨Tip Number 1
Familiarise yourself with the latest trends in quantitative finance, especially in alpha generation and market-making. This knowledge will not only help you during interviews but also demonstrate your genuine interest in the role.
✨Tip Number 2
Network with professionals in the quantitative finance space. Attend industry conferences or webinars where you can meet potential colleagues or mentors who can provide insights into the role and the company culture.
✨Tip Number 3
Brush up on your Python and KDB skills, as these are crucial for the position. Consider working on personal projects or contributing to open-source projects that showcase your technical abilities in these areas.
✨Tip Number 4
Prepare to discuss your previous experience in developing alpha models. Be ready to explain your thought process, the challenges you faced, and how you overcame them, as this will highlight your problem-solving skills and expertise.
We think you need these skills to ace Quantitative Researcher - Execution Services.
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience in quantitative finance, particularly in developing alpha models. Emphasise your proficiency in Python and KDB, as well as any relevant projects or research that align with the job description.
Craft a Strong Cover Letter: In your cover letter, explain why you are interested in the Quantitative Researcher position and how your background fits the role. Mention specific experiences related to return/toxicity forecasting and collaboration within teams.
Showcase Technical Skills: Include examples of your technical skills in your application. If you've worked on projects involving statistical and machine learning methods, describe these experiences and the outcomes achieved.
Highlight Educational Background: Clearly state your educational qualifications, especially if you hold a PhD or Master's degree in Statistics or a related field. This is crucial for demonstrating your theoretical understanding of the methods required for the role.
How to prepare for a job interview at Millennium Management
✨Showcase Your Modelling Skills
Be prepared to discuss your experience in designing and developing models for alpha generation. Highlight specific projects where you automated signal performance evaluation or combined multiple signals to produce usable alpha.
✨Demonstrate Statistical Rigor
Since the role requires avoiding overfitting and ensuring robust out-of-sample performance, be ready to explain your approach to statistical reasoning. Discuss any methods you've employed to maintain model integrity in complex scenarios.
✨Emphasise Collaboration Experience
Collaboration is key in this role. Share examples of how you've worked with team members and stakeholders to shape project direction and design. This will show your ability to work effectively within a team environment.
✨Technical Proficiency is Essential
Make sure to highlight your technical skills, especially in Python and KDB. Be ready to discuss specific instances where you've applied these skills in a quantitative finance setting, particularly in equities.
