At a Glance
- Tasks: Join a dynamic team to develop machine learning models for trading strategies.
- Company: Leading energy trading company at the forefront of technology and data science.
- Benefits: Competitive salary, career growth, and a culture that empowers you to excel.
- Other info: Collaborative environment with opportunities to work alongside senior leaders.
- Why this job: Make a real impact in trading with cutting-edge machine learning technologies.
- Qualifications: 3+ years in machine learning, strong Python skills, and a solid maths background.
The predicted salary is between 36000 - 60000 £ 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.
Quantitative Researcher Machine Learning - eFinancialCareers employer: eFinancialCareers
Quilter plc is an exceptional employer, offering a dynamic work environment in Southampton where innovation and collaboration thrive. With a strong commitment to employee growth, comprehensive benefits including a generous holiday allowance and a non-contributory pension scheme, Quilter fosters a culture of inclusivity and continuous improvement, empowering employees to make meaningful contributions to the financial futures of their clients and communities.
StudySmarter Expert Advice🤫
We think this is how you could land Quantitative Researcher Machine Learning - eFinancialCareers
✨Tip Number 1
Network like a pro! Get out there and connect with people in the industry. Attend meetups, conferences, or even online webinars. You never know who might have the inside scoop on job openings or can refer you directly to hiring managers.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your machine learning projects. Whether it's a GitHub repo or a personal website, having tangible examples of your work can really set you apart from the competition.
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge. Be ready to discuss your experience with algorithms, data processing, and Python coding. Practise explaining complex concepts in simple terms – it shows you really understand your stuff!
✨Tip Number 4
Don’t forget to apply through our website! We love seeing applications come directly from candidates who are genuinely interested in joining our team. Plus, it gives you a better chance to stand out in the crowd!
We think you need these skills to ace Quantitative Researcher Machine Learning - eFinancialCareers
Some tips for your application 🫡
Show Your Passion for Machine Learning:When you're writing your application, let your enthusiasm for machine learning shine through! We want to see how your experience aligns with our needs, so share specific projects or achievements that highlight your skills in ML algorithms and data processing.
Tailor Your CV and Cover Letter:Make sure to customise your CV and cover letter for the Quantitative Researcher role. Highlight relevant experiences and skills that match the job description. We love seeing candidates who take the time to connect their background with what we do at Dare!
Be Clear and Concise:Keep your application clear and to the point. We appreciate well-structured documents that are easy to read. Use bullet points where necessary and avoid jargon unless it’s relevant to the role. Remember, clarity is key!
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 shows you’re serious about joining our team at Dare!
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 methods in real-world scenarios, especially in trading or data-intensive environments.
✨Showcase Your Coding Skills
Since robust Python coding is crucial for this role, practice writing clean and efficient code. You might be asked to solve a problem on the spot, so being comfortable with coding challenges will give you an edge.
✨Understand the Business Context
Familiarise yourself with the energy trading sector and how machine learning can enhance trading strategies. Being able to connect your technical skills to the company's goals will demonstrate your strategic thinking.
✨Prepare for Collaboration Questions
As this role involves working closely with various teams, think of examples where you've successfully collaborated with others. Highlight your communication skills and how you've built relationships to drive projects forward.
