At a Glance
- Tasks: Design and develop machine learning models for financial data analysis.
- Company: Join TradingHub, a leader in intelligent trade surveillance software.
- Benefits: Competitive salary, hybrid work, private medical insurance, and generous leave policies.
- Other info: Diverse and inclusive workplace with excellent growth opportunities.
- Why this job: Be the first dedicated ML hire and make a real impact in finance.
- Qualifications: Experience in Python, LLMs, and financial datasets is essential.
The predicted salary is between 63000 - 77000 £ per year.
Founded in 2010, TradingHub delivers uniquely intelligent trade surveillance software to world leading financial institutions. With a team of over 150 experts worldwide, TradingHub combines global reach with deep markets expertise to help our customers mitigate financial, regulatory, and reputational risk.
We're looking for a Machine Learning Engineer to join our Analytics division and play an important role in enhancing our metrics offering. As our first dedicated ML hire, you’ll be utilising an array of modern LLM and NLP techniques to analyse complex financial data and unlock new capabilities for our market-leading suite of trade surveillance products.
This role will see you combine hands-on model development and software engineering, and collaborate with a high-performing team of Quant Researchers and Developers as well as other cross-functional departments.
- Design, develop, and deploy machine learning models to enhance TradingHub’s market surveillance and analytics platform
- Apply machine learning and statistical techniques to large-scale financial datasets, improving accuracy and reducing false positives
- Leverage LLM and NLP models to extract insights from unstructured data and integrate them into existing analytics workflows
- Collaborate closely with quantitative developers, data engineers, and product teams to productionise models into scalable, high-performance systems
- Confident programming skills in Python, with experience using modern ML frameworks
- Good understanding of core machine learning concepts such as linear regression, reinforcement learning and deep learning
- Industry experience using Large Language Models (LLMs) to deliver commercial value
- Experience building data pipelines and performing feature engineering on real-world datasets
- Good understanding of SQL and working with complex datasets
- Keen interest in financial markets e.g. pricing, trading, fixed income
We are dedicated to building a diverse, equitable and inclusive workplace, so if this role excites you, please don't let our specification hold you back. We do not discriminate based on race, religion, ethnic or national origins, sex (including pregnancy, childbirth, reproductive health decisions, or related medical conditions), sexual orientation, gender identity, gender expression, age, socio-economic background, responsibilities for dependants, physical or mental disability or other applicable legally protected characteristics.
Machine Learning Engineer (hybrid or remote) in London employer: TradingHub
At TradingHub, we pride ourselves on being an exceptional employer, offering a dynamic work culture that fosters innovation and collaboration among our talented team of over 150 experts. Located in the heart of London, we provide competitive compensation, comprehensive benefits including private medical insurance, generous parental leave, and opportunities for professional growth through study leave for qualifications. Join us to be part of a diverse and inclusive environment where your unique skills can thrive and contribute to cutting-edge financial solutions.
StudySmarter Expert Advice🤫
We think this is how you could land Machine Learning Engineer (hybrid or remote) in London
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We think you need these skills to ace Machine Learning Engineer (hybrid or remote) 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!
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Craft a Tailored Cover Letter:For a full-time role at TradingHub, 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 TradingHub. 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 TradingHub
✨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 TradingHub!
✨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.
