ML Engineer, Recommender Systems β€” Scalable Production in England

ML Engineer, Recommender Systems β€” Scalable Production in England

England Full-Time 50000 - 65000 Β£ / year (est.) No working from home possible
ASOS

At a Glance

  • Tasks: Develop and deploy impactful machine learning solutions for real-world business applications.
  • Company: Join ASOS, a dynamic and innovative fashion retailer.
  • Benefits: Enjoy a competitive salary, generous discounts, and structured development opportunities.
  • Other info: Collaborative team environment with exciting career growth potential.
  • Why this job: Make a real impact by optimising models and deploying systems at scale.
  • Qualifications: Hands-on experience with deep learning and modern ML frameworks required.

The predicted salary is between 50000 - 65000 Β£ per year.

ASOS seeks a Machine Learning Engineer in England to join a dynamic team focusing on developing and deploying impactful machine learning solutions. The role involves optimizing models for real-world business use and deploying machine learning systems at scale.

The ideal candidate will have hands-on experience with deep learning, modern ML frameworks, and a collaborative mindset.

ASOS offers a competitive salary, generous employee discounts, and structured development opportunities.

ML Engineer, Recommender Systems β€” Scalable Production in England employer: ASOS

ASOS is an excellent employer for those looking to make a significant impact in the field of machine learning. With a vibrant work culture that fosters collaboration and innovation, employees benefit from competitive salaries, generous discounts, and structured opportunities for professional growth. Located in England, ASOS provides a unique environment where creativity meets technology, making it an ideal place for aspiring ML Engineers to thrive.

ASOS

Contact Details:

ASOS Recruitment Team

StudySmarter Expert Advice🀫

We think this is how you could land ML Engineer, Recommender Systems β€” Scalable Production in England

✨Get Involved in Data Science Meetups

Tap into local data science meetups or workshops to connect with fellow enthusiasts and professionals. These events are goldmines for networking, and sometimes even lead directly to job openings at companies like ASOS!

✨Show Off Your Projects

Start building a public portfolio showcasing your data science projects on platforms like GitHub or personal websites. Highlight unique analyses or models you've developed. This not only demonstrates your skills but also gets your name out there for roles like ML Engineer, Recommender Systems β€” Scalable Production at ASOS.

✨Leverage Professional Networks

Join professional bodies related to data science, like the Data Science Society or similar organisations. Getting involved can lead to mentorship opportunities and insider knowledge about full-time positions at companies like ASOS.

✨Apply Directly through Our Website

When you find a suitable opening like ML Engineer, Recommender Systems β€” Scalable Production at ASOS, make sure to apply directly through our website. It gives you an edge and shows you're keen to join our team. Plus, who doesn’t love a direct application? It’s easier than navigating through job boards!

We think you need these skills to ace ML Engineer, Recommender Systems β€” Scalable Production in England

Machine Learning
Deep Learning
Model Optimization
ML Frameworks
Scalable Systems Deployment
Collaborative Mindset
Real-World Business Application

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!

Quantify Your Achievements:Employers love numbers! When drafting your CV, highlight your achievements with quantifiable results. For instance, mention how your data analysis led to a certain percentage increase in efficiency or revenue at a previous job or project. These details can really make your application pop!

Craft a Tailored Cover Letter:For a full-time role at ASOS, 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 ASOS. 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 ASOS

✨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 ASOS!

✨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.