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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 transforming the investment landscape with AI technology.
- Qualifications: Strong understanding of data analysis, machine learning, and statistical methods.
- 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.
- Utilizing 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.
Machine Learning Engineer employer: Mintus
Contact Detail:
Mintus Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Machine Learning Engineer
✨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 in the industry. You never know who might have a lead on your dream job!
✨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 crowd.
✨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 machine learning interview questions and think about how you can relate your experience to the role at Mintus.
✨Tip Number 4
Apply through our website! We love seeing applications directly from candidates who are genuinely interested in joining us. Tailor your application to highlight how your skills align with what we’re looking for in a Machine Learning Engineer.
We think you need these skills to ace Machine Learning Engineer
Some tips for your application 🫡
Tailor Your CV: Make sure your CV is tailored to the Machine Learning Engineer role. Highlight your experience with data analysis, machine learning systems, 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. Be sure to mention specific experiences that relate to the job description.
Showcase Your Projects: If you've worked on any machine learning projects, make sure to include them in your application. Whether it's a personal project or something from your previous job, we love seeing practical examples of your work and how you’ve tackled complex problems.
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way to ensure your application gets into the right hands. Plus, it shows us you're genuinely interested in joining our team at Mintus!
How to prepare for a job interview at Mintus
✨Know Your ML Fundamentals
Brush up on your machine learning fundamentals before the interview. Be prepared to discuss various algorithms, their applications, and how you’ve used them in past projects. This will show that you have a solid understanding of the core concepts that Mintus values.
✨Showcase Your Problem-Solving Skills
Prepare to discuss specific examples where you've tackled complex data problems. Highlight your approach to statistical analysis and how you optimised existing models. This will demonstrate your ability to think critically and apply your knowledge effectively.
✨Familiarise Yourself with Big Data Technologies
Since the role involves working with large datasets, make sure you’re familiar with big data technologies relevant to the position. Be ready to talk about your experience with tools like Hadoop or Spark, and how they can enhance data management and model performance.
✨Collaborate and Communicate
Mintus values collaboration, so be prepared to discuss how you’ve worked with data analysts and engineers in the past. Emphasise your communication skills and how you ensure everyone is aligned on project objectives, as this is crucial for success in a team environment.
