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For Companies At a Glance
- Tasks: Design and implement portfolio optimisation models to enhance investment strategies.
- Company: Join Larson Maddox, a leader in quantitative finance innovation.
- Benefits: Competitive salary, dynamic work environment, and opportunities for professional growth.
- Why this job: Make a real impact in finance with cutting-edge quantitative techniques.
- Qualifications: Advanced degree in Quantitative Finance or related field; strong programming skills required.
- Other info: Collaborative team culture with exposure to diverse investment strategies.
The predicted salary is between 36000 - 60000 Ā£ per year.
We are seeking a highly skilled Portfolio Optimisation Quant to join our investment team. The successful candidate will play a key role in designing, implementing, and maintaining portfolio construction and optimisation frameworks that enhance riskāadjusted returns across multiple strategies. This is an opportunity to work in a dynamic, fastāpaced environment where quantitative innovation drives investment decisions.
Key Responsibilities
- Develop and maintain portfolio optimisation models using advanced quantitative techniques (e.g., meanāvariance optimisation, risk parity, factorābased approaches).
- Implement risk management frameworks, including stress testing, scenario analysis, and liquidity constraints.
- Collaborate with portfolio managers and researchers to integrate optimisation tools into the investment process.
- Analyse large datasets to identify patterns, correlations, and actionable insights for portfolio construction.
- Enhance existing optimisation algorithms to incorporate transaction costs, turnover constraints, and regulatory requirements.
- Build and maintain productionālevel code for optimisation systems in Python/C++ or similar languages.
- Monitor and improve performance attribution and risk decomposition across portfolios.
- Stay upātoādate with academic research and industry best practices in portfolio theory and quantitative finance.
Required Skills & Qualifications
- Advanced degree (Master's or PhD) in Quantitative Finance, Mathematics, Statistics, Computer Science, or related field.
- Strong understanding of portfolio theory, optimisation techniques, and risk management principles.
- Proficiency in Python, C++, or similar programming languages; experience with numerical libraries (e.g., NumPy, Pandas, SciPy).
- Solid knowledge of linear algebra, convex optimisation, and stochastic processes.
- Experience with data analysis, machine learning, or factor modelling is a plus.
- Familiarity with market microstructure, transaction cost modelling, and liquidity constraints.
- Excellent communication skills and ability to work collaboratively with investment professionals.
Preferred Experience
- Previous experience in a hedge fund, asset management firm, or proprietary trading environment.
- Exposure to multiāasset portfolios, including equities, fixed income, derivatives, and alternative investments.
- Knowledge of cloud computing and distributed systems for largeāscale optimisation.
Quantitative Researcher - Larson Maddox in London employer: Jobs via eFinancialCareers
Contact Detail:
Jobs via eFinancialCareers Recruiting Team
StudySmarter Expert Advice š¤«
We think this is how you could land Quantitative Researcher - Larson Maddox in London
āØTip Number 1
Network like a pro! Reach out to professionals in the finance and quantitative research fields. Use platforms like LinkedIn to connect with people at Larson Maddox or similar firms. A friendly chat can sometimes lead to job opportunities that aren't even advertised!
āØTip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those involving Python or C++. This could be anything from optimisation models to data analysis. When you apply through our website, include links to your work so potential employers can see what you're capable of.
āØTip Number 3
Prepare for interviews by brushing up on your technical knowledge. Be ready to discuss portfolio theory, optimisation techniques, and risk management principles. Practise explaining complex concepts in simple terms ā it shows you really understand your stuff and can communicate effectively with the team.
āØTip Number 4
Stay updated with industry trends! Read up on the latest research in quantitative finance and portfolio optimisation. Being knowledgeable about current practices will not only help you in interviews but also show your passion for the field when you apply through our website.
We think you need these skills to ace Quantitative Researcher - Larson Maddox in London
Some tips for your application š«”
Tailor Your CV: Make sure your CV is tailored to the Portfolio Optimisation Quant role. Highlight your experience with portfolio theory, optimisation techniques, and any relevant programming skills. We want to see how your background aligns with what we're looking for!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you're passionate about quantitative finance and how your skills can contribute to our investment team. Keep it concise but impactful ā we love a good story!
Showcase Your Technical Skills: Since this role involves advanced quantitative techniques, make sure to showcase your proficiency in Python, C++, or similar languages. Mention any projects or experiences where you've applied these skills, especially in portfolio optimisation or risk management.
Apply Through Our Website: We encourage you to apply through our website for a smoother application process. It helps us keep track of your application and ensures you donāt miss out on any important updates. Plus, itās super easy!
How to prepare for a job interview at Jobs via eFinancialCareers
āØKnow Your Quantitative Stuff
Make sure you brush up on your portfolio theory and optimisation techniques. Be ready to discuss advanced concepts like mean-variance optimisation and risk management frameworks. Theyāll want to see that you can talk the talk and walk the walk!
āØShow Off Your Coding Skills
Since you'll be building production-level code, itās crucial to demonstrate your proficiency in Python or C++. Bring examples of your previous work or projects that showcase your coding abilities, especially with numerical libraries like NumPy or Pandas.
āØPrepare for Data Analysis Questions
Expect questions that test your ability to analyse large datasets. Brush up on your data analysis skills and be prepared to discuss how you've identified patterns or insights in past roles. Theyāll want to know how you can apply this to portfolio construction.
āØCommunicate Clearly and Collaboratively
Since collaboration is key in this role, practice articulating your thoughts clearly. Be ready to discuss how youāve worked with portfolio managers or researchers in the past. Good communication can set you apart from other candidates!
