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- Tasks: Design and optimise machine learning systems to revolutionise financial decision-making.
- Company: Mintus, a pioneering fintech company in London.
- Benefits: Competitive salary, flexible working hours, and opportunities for professional growth.
- Why this job: Join a cutting-edge team and make a real impact in the fintech industry.
- Qualifications: Degree in computer science or related field; experience in machine learning required.
- Other info: Dynamic work environment with a focus on innovation and collaboration.
The predicted salary is between 36000 - 60000 £ per year.
Mintus is a pioneering fintech company based in London, dedicated to revolutionizing the alternative investment landscape with state-of-the-art AI platforms to enable financial institutions to expand asset classes, improve efficiency, and enhance investment performance. We are looking for a highly capable machine learning engineer to optimize and enhance our machine learning systems. You will be responsible for evaluating existing processes, performing statistical analysis, and enhancing the accuracy of our ML models, ensuring they remain relevant and up to date.
The ideal candidates must possess a broad understanding of data analysis and data engineering, enabling them to perform rigorous statistical analysis to resolve dataset problems and manage complex data modeling. They will translate their expertise into enhanced predictive automation software that transforms decision-making for global financial institutions.
Responsibilities:
- Consulting with managers to determine and refine machine learning objectives.
- Designing machine learning systems and self-running artificial intelligence (AI) software to automate predictive models.
- Utilising semantic modeling to improve the management of complex financial data sets.
- Collaborating with Data Analysts and engineers for data solutions for modelling purposes.
- Transforming data science prototypes and applying appropriate ML algorithms and tools.
- Ensuring that algorithms generate accurate user recommendations.
- Familiarity with big data technologies and selecting appropriate datasets and data representation methods.
- Solving complex problems with multi-layered data sets, as well as optimizing existing machine learning libraries and frameworks.
- Developing ML algorithms on large volumes of historical data for predictions.
- Running tests, performing statistical analysis, and interpreting test results.
- Documenting machine learning processes.
- Model monitoring in Production utilizing appropriate metrics and reporting.
Requirements & Skills:
- Degree in computer science, data science, mathematics, or a related field.
- Master’s degree in data analytics, or similar will be advantageous.
- At least two years' experience as a machine learning engineer.
- Advanced proficiency with Python, Java, and R code writing.
- Extensive knowledge of ML frameworks, libraries, data structures, data modeling, and software architecture.
- In-depth knowledge of mathematics, statistics, and algorithms.
- Strong analytical and problem-solving abilities and high attention to detail.
- Great communication and collaboration skills.
- Excellent time management and organizational abilities.
Machine Learning Engineer in London employer: Mintus
Contact Detail:
Mintus Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Machine Learning Engineer in London
✨Tip Number 1
Network like a pro! Reach out to people in the fintech and machine learning space. Attend meetups, webinars, or even just grab a coffee with someone who’s already in the industry. You never know where a casual chat might lead!
✨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, make sure it highlights your best work. This is your chance to shine beyond the CV!
✨Tip Number 3
Prepare for those interviews! Brush up on your technical skills and be ready to discuss your past projects in detail. Practice common ML interview questions and think about how you can relate your experience to the role at Mintus.
✨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 take that extra step to connect with us directly.
We think you need these skills to ace Machine Learning Engineer in London
Some tips for your application 🫡
Tailor Your CV: Make sure your CV is tailored to the Machine Learning Engineer role. Highlight your experience with ML frameworks, data analysis, and any relevant projects you've worked on. We want to see how your skills align with what we're looking for!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you're passionate about machine learning and how you can contribute to our mission at Mintus. Keep it concise but impactful – we love a good story!
Showcase Your Projects: If you've worked on any cool machine learning projects, make sure to mention them! Whether it's a personal project or something from your previous job, we want to see your hands-on experience and creativity in action.
Apply Through Our Website: We encourage you to apply through our website for the best chance of getting noticed. It’s super easy, and you'll be able to submit all your documents in one go. Plus, we love seeing applications come directly from our site!
How to prepare for a job interview at Mintus
✨Know Your ML Stuff
Make sure you brush up on your machine learning concepts and frameworks. Be ready to discuss specific algorithms you've worked with, how you optimised them, and the impact they had on previous projects. This shows you're not just familiar with the theory but can apply it in real-world scenarios.
✨Showcase Your Problem-Solving Skills
Prepare to talk about complex problems you've solved using data analysis and machine learning. Use the STAR method (Situation, Task, Action, Result) to structure your answers. This will help interviewers see your thought process and how you tackle challenges head-on.
✨Collaborate Like a Pro
Since collaboration is key in this role, think of examples where you've worked with data analysts or engineers. Highlight how you communicated your ideas and contributed to team success. This will demonstrate your ability to work well in a team environment, which is crucial for the job.
✨Ask Smart Questions
At the end of the interview, don’t forget to ask insightful questions about the company's machine learning objectives and the technologies they use. This shows your genuine interest in the role and helps you gauge if it's the right fit for you.
