At a Glance
- Tasks: Conduct cutting-edge research and develop innovative quantitative models in a dynamic team.
- Company: Exciting new Quant Hedge Fund with a relaxed, open culture.
- Benefits: Competitive salary, flexible work environment, and opportunities for personal growth.
- Other info: Work in a calm atmosphere with a focus on creativity and innovation.
- Why this job: Join a top-tier team and make a real impact in the finance tech space.
- Qualifications: PhD in Maths, Stats, or Comp Sci; strong data visualisation and programming skills.
The predicted salary is between 59400 - 72600 £ per year.
Quant Researcher Quant Capital is urgently looking for Quant Researcher to join our high profile client.
Our client is a new growing Quant Hedge Fund in the Commodities and Equities space.
The ideal Quant candidate will be highly proficient at researching data and pattern recognition, but may You will be a researcher or mathematician motivated by what’s new to ensure you’re always ahead of the curve.
You will understand the need and importance of strong research and strong modelling.
The Quant Team is 14 at present so research is both team and individually focussed.
We are looking for those at the top tier of the Quantitative market.
Key experience I am looking for is data visualisation in a complex system environment as well as time series analysis.
On a day to day you will: Working on your own strategy research Back Testing models Working with the development team to execute models.
Execution Latency strategy Research Machine learning research Quant Research Candidates MUST have: (the below also gives you an idea of stack)Ph D in Maths Stats or Comp Sci from a top school MIT Oxbridge or Imperial (Southampton Comp Sci)Good understanding of Stats and time series Understanding of relational and non-relational databases with or without using an ORMR or Matlab ideally C++ or Python Interest in quantitative problems The environment is that of Facebook or Google, relaxed open with time to think and make the right decisions.
The atmosphere is calm and relaxed with an open dress code.
This is a role for scientists and those who are motivated by the sharp end of technology and the possibility of making serious money doing something you are passionate about.
My client is based in Cambridge If this sounds of interest then please apply directly.
Quant Research C++ Matlab, R, Python Data Science, python, Bubble visualisation Linux AWS Cloud Systems Infrastructure Linux Sys Admin Financial Tech STartup – Linux, Scripting, Automation, Kernel, No SQL DEVOPS Python Development Operations
Quant Researcher in London employer: Quant Capital
Quant Capital is an excellent employer for those looking to thrive in the fintech sector, offering a vibrant work culture that fosters innovation and collaboration. With substantial training and development opportunities, employees can enhance their skills while enjoying a flexible hybrid work model in the heart of London. Join us to be part of a forward-thinking team that values growth and cutting-edge technology.
StudySmarter Expert Advice🤫
We think this is how you could land Quant Researcher in London
✨Get Involved in Data Science Meetups
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Join professional bodies related to data science, like the Data Science Society or similar organisations. Getting involved can lead to mentorship opportunities and insider knowledge about full-time positions at companies like Quant Capital.
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We think you need these skills to ace Quant Researcher in London
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
Quantify Your Achievements:Employers love numbers! When drafting your CV, highlight your achievements with quantifiable results. For instance, mention how your data analysis led to a certain percentage increase in efficiency or revenue at a previous job or project. These details can really make your application pop!
Craft a Tailored Cover Letter:For a full-time role at Quant Capital, your cover letter should reflect your passion for data science and your excitement about the specific projects or values of the company. Dive into why you’re a good fit, how your skills align with their needs, and any unique perspectives you can bring to the team.
Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at Quant Capital. Mention any standout courses you've completed that equipped you with essential skills, such as machine learning certifications or data visualisation courses. This shows your commitment to continuously developing your skills in the field!
How to prepare for a job interview at Quant Capital
✨Brush Up on Your Statistics
For a data science role, we need to seriously sharpen our statistics skills. Get ready to tackle technical questions on probability distributions, hypothesis testing, and regression analysis. These are often the bread and butter of data science interviews, so don't just skim over them!
✨Showcase Your Projects
Prepare a killer portfolio showcasing your data science projects. We should include details about the datasets used, the tools and techniques applied, and the impact of your findings. If we can walk them through a particularly challenging project or a cool visualisation that had real-world implications, it’ll really make us stand out!
✨Get Comfortable with Python and R
Most data science positions require us to be proficient in programming languages like Python and R. We should practice common libraries like pandas, NumPy, and scikit-learn, and be ready for live coding exercises or algorithm questions. Showing off our coding chops can really impress the interviewers at Quant Capital!
✨Prepare for Case Studies
Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.
