At a Glance
- Tasks: Build ML-driven insight systems using financial time series data and develop impactful models.
- Company: Data-driven fintech firm in London with a focus on innovation.
- Benefits: Competitive salary up to £200,000, equity options, and a dynamic work environment.
- Other info: In-office role with opportunities for professional growth and development.
- Why this job: Directly influence investor decisions and work on cutting-edge financial technology.
- Qualifications: Strong Python skills and over 5 years in quantitative analysis or financial modelling.
The predicted salary is between 110000 - 200000 £ per year.
A data-driven financial technology firm in London seeks a Quantitative Analyst to build ML-driven insight systems using structured financial time series data. This role requires strong Python skills and over 5 years of experience in quantitative analysis or financial modeling.
You will develop models, deploy them into production, and evaluate signals based on real-world investor outcomes. The compensation ranges from £110,000 to £200,000 depending on experience, with equity included. This is an in-office role that offers direct influence on investor decision-making.
Senior ML Quant Engineer: Time-Series Signals (London) employer: Reflexivity
Join a leading data-driven financial technology firm in London, where innovation meets opportunity. With a strong emphasis on employee growth and a collaborative work culture, we offer competitive compensation packages, including equity, and the chance to make a tangible impact on investor decision-making. Our commitment to fostering talent ensures that you will thrive in an environment that values your expertise and encourages continuous learning.
StudySmarter Expert Advice🤫
We think this is how you could land Senior ML Quant Engineer: Time-Series Signals (London)
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, attend meetups, and connect with potential colleagues on LinkedIn. You never know who might have the inside scoop on job openings or can put in a good word for you.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your best projects, especially those involving time-series analysis and machine learning. This will give you an edge and demonstrate your hands-on experience to potential employers.
✨Tip Number 3
Prepare for interviews by brushing up on your Python skills and understanding financial modelling concepts. Practice common interview questions related to quantitative analysis and be ready to discuss your past projects in detail.
✨Tip Number 4
Don’t forget to apply through our website! We’ve got loads of opportunities that might just be the perfect fit for you. Plus, it’s a great way to ensure your application gets seen by the right people.
We think you need these skills to ace Senior ML Quant Engineer: Time-Series Signals (London)
Some tips for your application 🫡
Show Off Your Python Skills:Make sure to highlight your Python expertise in your application. We want to see how you've used it in past projects, especially in quantitative analysis or financial modelling. Don't just say you're good at it; give us examples!
Quantitative Analysis Experience is Key:Since we're looking for someone with over 5 years of experience, be sure to detail your relevant work history. We love seeing how you've tackled complex problems and what impact your work has had on investor outcomes.
Tailor Your Application:Take a moment to customise your application for this role. Mention specific projects or experiences that align with building ML-driven insight systems. This shows us you’re genuinely interested and have done your homework!
Apply Through Our Website:We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you don’t miss out on any important updates. Plus, we love seeing applications come through our own channels!
How to prepare for a job interview at Reflexivity
✨Know Your Python Inside Out
Make sure you brush up on your Python skills, especially libraries like Pandas and NumPy. Be ready to discuss how you've used these tools in past projects, particularly in quantitative analysis or financial modelling.
✨Showcase Your Time-Series Expertise
Prepare to talk about your experience with time-series data. Have examples ready that demonstrate how you've built models or evaluated signals based on real-world outcomes. This will show your understanding of the specific challenges in this area.
✨Understand the Financial Landscape
Familiarise yourself with current trends in financial technology and how machine learning is being applied. Being able to discuss recent developments will impress the interviewers and show your passion for the field.
✨Prepare Questions That Matter
Think of insightful questions to ask about the company's approach to ML-driven insights and investor decision-making. This not only shows your interest but also helps you gauge if the company aligns with your career goals.
