At a Glance
- Tasks: Design and improve sports prediction models and high-frequency trading strategies.
- Company: Join a tech-driven trading firm with a startup vibe and a focus on innovation.
- Benefits: Competitive salary, bonuses, flexible working, and ongoing training opportunities.
- Other info: Dynamic environment with excellent career growth and a commitment to excellence.
- Why this job: Work with smart people and cutting-edge tools to make a real impact in finance.
- Qualifications: Degree in Maths, Stats, or Engineering; strong Python and stats background required.
The predicted salary is between 50000 - 50000 £ per year.
Junior Quant Analyst – Stats 50,000 Plus Bonus Quant Capital is urgently looking for a Quant Analyst to join our high profile client in their main quant research team.
Our client is well-known tech-driven trading firm, they provide software to the market and also prop trade.
This is an environment of google or a startup where tech is number 1.
The firm is known globally for its attitudes and rigour more importantly, you will be surrounded by smart people deeply interested in teaching what they know, and in learning from you.
You will join the quant team, designing, analysing and improving sport prediction models and high frequency trading strategies.
You’ll be part of a small team, working in a relaxed environment, and using a range of modern open-source tools including Jupyter, Py Torch and Spark.
You’ll have the opportunity to work with terabytes of data, and will be part of a team that can quickly run experiments at scale in the markets, allowing us to test your hypotheses in a live environment.
On a day-to-day basis your responsibilities will include
- Building and improving sports prediction models
- Implementing and backtesting new statistical arbitrage strategies
- Developing and improving clients’ high frequency strategies
- Supporting the trading desk with analysis of strategy performance or A/B testing data.
- Quant Analysts
- Must
- Ideally
Have
- Comp Ski, Maths, Stats or Engineering degree.
- Demonstrable Python experience
- Strong Stats Background
- Outstanding background in probability and statistics
- Experience and interest in modern machine learning techniques, for example, deep learning, Bayesian methods and graphical models
- Programming experience with python, julia or R You will be joining a progressive and exciting company committed to excellence.
They offer an excellent working environment with ongoing training and a structured career path.
You can also expect an excellent salary and benefits package, flex working.
Including Options from day 1.
My client is based in West London Flex working.• Quant Research, Gauss, Hull, Options Theory, C++ Erlang, F#, Scala, Haskell, Django, Python, Twisted, no SQL, Data Science
Junior Quant Analyst – Stats 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 Junior Quant Analyst – Stats in London
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We think you need these skills to ace Junior Quant Analyst – Stats 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.
