At a Glance
- Tasks: Join a dynamic team to develop machine learning models for trading strategies.
- Company: Leading energy trading company with a focus on innovation and collaboration.
- Benefits: Competitive salary, health insurance, 38 days holiday, and personal development budget.
- Other info: Enjoy a vibrant workplace culture with social events and a games room.
- Why this job: Make a real impact in a fast-paced environment while working with cutting-edge technology.
- Qualifications: 3+ years in machine learning, strong Python skills, and a solid maths background.
The predicted salary is between 60000 - 80000 € per year.
City of London
Permanent, Full-time - Onsite
Who we are:
We are an energy trading company generating liquidity across global commodities markets. We combine deep trading expertise with proprietary technology and the power of data science to be the best-in-class. Our understanding of volatile, data-intensive markets is a key part of our edge. At Dare, you will be joining a team of ambitious individuals who challenge themselves and each other. We have a culture of empowering exceptional people to become the best version of themselves.
What you’ll be doing:
The Quantitative Researcher is a key role within the algorithmic technical space at Dare. Working closely with a talented algorithmic and technical team to build a platform that delivers ML capabilities to our Liquidity trading teams. These teams are responsible for delivering products for internal customers. Setting and delivering a consistent, scalable approach to machine learning across the organisation is one of the key success criteria for this role. The role requires building relationships and collaborating with Senior Leaders across the business to shape a strategy that delivers models that provide our traders with a competitive edge.
- Using Dare’s proprietary trading data and models to drive trading PNL.
- Developing trading indicators and strategies powered by machine learning.
- Partnering with quantitative research and algorithmic trading technology teams.
- Collaborating with the CEO and other senior stakeholders to combine domain knowledge with engineering expertise.
What you’ll bring:
- 3+ years experience in machine learning algorithms, software engineering, and data mining models, with large language modelling (LLM) experience being advantageous.
- A background in maths, statistics, and algorithms, with the capability to write robust scalable Python code.
- A strong understanding of the mathematical and statistical fundamentals on which the ML methods are based.
- Experience with production data processing, including data manipulation, data cleansing, aggregation, efficient (pre-)processing, etc.
- Experience with time-series data, including storage and management.
- A strong understanding through the usage of machine learning frameworks (TensorFlow, PyTorch, sci-kit-learn, Huggingface).
- Ability to work with analytical teams to build dashboards that prove the value of the machine learning capabilities as we deliver models to our production environments.
Desirable:
- Experience working with real-time data systems.
- Experience working with cloud-based solutions.
Benefits & perks:
- Competitive salary
- Vitality health insurance and dental cover
- 38 days of holiday (including bank holidays)
- Pension scheme
- Annual Bluecrest health checks
- A personal learning & development budget of £5000
- Free gym membership
- Specsavers vouchers
- Enhanced family leave
- Cycle to Work scheme
- Credited Deliveroo dinner account
- Office massage therapy
- Freshly served office breakfast twice a week
- Fully stocked fridge and pantry
- Social events and a games room
Diversity matters:
We believe in a workplace where our people can fulfill their potential, whatever their background or whomever they are. We celebrate the breadth of experience and see this as critical to problem-solving and to Dare thriving as a business. Our culture rewards curiosity and drive, so the best ideas triumph and everyone here can make an impact.
Please let us know ahead of the interview and testing processes if you require any reasonable adjustments or assistance during the application process. We’re also proud to be certified a ‘Great Place to Work’.
Quantitative Researcher (Machine Learning) - eFinancialCareers in London employer: eFinancialCareers
At Dare, we pride ourselves on being an exceptional employer in the heart of the City of London, where innovation meets collaboration. Our vibrant work culture fosters personal and professional growth, offering a generous benefits package that includes 38 days of holiday, a £5000 learning budget, and wellness initiatives like office massage therapy and gym memberships. Join us to be part of a dynamic team that empowers you to excel in your role as a Quantitative Researcher, while making a meaningful impact in the fast-paced world of energy trading.
StudySmarter Expert Advice🤫
We think this is how you could land Quantitative Researcher (Machine Learning) - eFinancialCareers in London
✨Tip Number 1
Network like a pro! Reach out to people in the industry, especially those at companies you're interested in. A friendly chat can open doors and give you insights that job descriptions just can't.
✨Tip Number 2
Prepare for interviews by practising common questions and scenarios related to machine learning and quantitative research. We recommend doing mock interviews with friends or using online platforms to get comfortable.
✨Tip Number 3
Showcase your skills through projects or a portfolio. If you've built any ML models or worked on relevant data projects, make sure to highlight them during interviews. It’s all about demonstrating what you can bring to the table!
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, we love seeing candidates who are proactive about their job search.
We think you need these skills to ace Quantitative Researcher (Machine Learning) - eFinancialCareers in London
Some tips for your application 🫡
Tailor Your CV:Make sure your CV is tailored to the Quantitative Researcher role. Highlight your experience with machine learning algorithms and Python coding, as these are key for us. Use specific examples that showcase your skills in data processing and model development.
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Tell us why you're passionate about machine learning and how your background aligns with our mission at Dare. Be sure to mention any relevant projects or experiences that demonstrate your expertise.
Showcase Your Technical Skills:We love seeing your technical prowess! Include details about the machine learning frameworks you've worked with, like TensorFlow or PyTorch. If you have experience with time-series data or cloud-based solutions, make sure to highlight that too!
Apply Through Our Website:Don't forget to apply through our website! It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it gives you a chance to explore more about our company culture and values.
How to prepare for a job interview at eFinancialCareers
✨Know Your Algorithms
Make sure you brush up on your machine learning algorithms and their mathematical foundations. Be prepared to discuss how you've applied these in real-world scenarios, especially in trading contexts. This will show that you understand the methods deeply, not just the surface-level functions.
✨Showcase Your Coding Skills
Since robust Python coding is crucial for this role, be ready to demonstrate your coding abilities. You might be asked to solve a problem on the spot, so practice writing clean, efficient code that reflects your understanding of data manipulation and processing.
✨Prepare for Technical Questions
Expect technical questions related to time-series data and machine learning frameworks like TensorFlow or PyTorch. Review your past projects and be ready to explain your thought process and the impact of your work on trading strategies.
✨Build Relationships
This role involves collaboration with senior leaders and various teams. Think about how you can demonstrate your ability to build relationships and communicate effectively. Prepare examples of how you've successfully worked with others to achieve common goals in previous roles.
