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- Tasks: Build cutting-edge machine learning models for personalised recommendations on Depop's app.
- Company: Join a dynamic team at Depop, where innovation meets collaboration.
- Benefits: Enjoy flexible working, health benefits, and generous leave policies.
- Why this job: Make a real impact by enhancing user experiences for millions daily.
- Qualifications: Experience in machine learning and proficiency in Python required.
- Other info: Opportunities for growth, mentorship, and a supportive work environment.
The predicted salary is between 36000 - 60000 £ per year.
At Depop, machine learning is integral to delivering a personalised experience for our users. As a Machine Learning Scientist, you will work on building state-of-the-art models to power Depop's app, serving millions of personalised recommendations to users daily. You'll work on recommendation problems such as representation learning, cold start users and listings, tuning relevance, diversity & serendipity. The solutions you build will primarily use large volume data and deep learning techniques to deliver recommendation systems that operate at scale with high performance.
Responsibilities
- Research, design and deliver machine learning solutions to improve our recommendations
- Work closely with Product, Insights and Engineering partners to deliver value driving improvements to our recommendation systems
- Set up and conduct large-scale experiments to test hypotheses and drive product development
- Stay up to date with research in recommendation systems and modern deep learning, applying new techniques where appropriate
- Participate in team ceremonies including agile rituals, technical design discussions, and roadmap planning
- Clearly communicate technical approaches, results, and trade-offs to both technical and non-technical partners
Qualifications
- Experience working as a Machine Learning Scientist, with a track record of delivering models to solve industry-scale problems
- Solid understanding of machine learning concepts, familiarity working with common frameworks such as Transformers, PyTorch or TensorFlow
- Proficiency in Python, with the ability to write production-grade code and a good understanding of data engineering & MLOps
- Collaborative and humble team player with an ability to work with cross-functional teams, including technical and non-technical stakeholders
- Passion for learning new skills and staying up-to-date with ML algorithms
Bonus points
- Experience working on learning-to-rank, search or recommendation models
- Experience with deep learning & large language models
- Experience with experiment design and conducting A/B tests
- Experience with Databricks and PySpark
- Experience working with AWS or another cloud platform (GCP/Azure)
Additional information
- Health + Mental Wellbeing: PMI and cash plan healthcare access with Bupa; Subsidised counselling and coaching with Self Space
- Cycle to Work scheme with options from Evans or the Green Commute Initiative
- Employee Assistance Programme (EAP) for 24/7 confidential support
- Mental Health First Aiders across the business for support and signposting
- Work/Life Balance: 25 days annual leave with option to carry over up to 5 days; 1 company-wide day off per quarter
- Impact hours: Up to 2 days additional paid leave per year for volunteering
- Fully paid 4 week sabbatical after completion of 5 years of consecutive service with Depop
- Flexible Working: MyMode hybrid-working model with Flex, Office Based, and Remote options (role dependant)
- All offices are dog-friendly
- Ability to work abroad for 4 weeks per year in UK tax treaty countries
- Family Life: 18 weeks of paid parental leave for full-time regular employees; IVF leave, shared parental leave, and paid emergency parent/carer leave
- Learn + Grow: Budgets for conferences, learning subscriptions, and more; Mentorship and programmes to upskill employees
- Your Future: Life Insurance (financial compensation of 3x your salary); Pension matching up to 6% of qualifying earnings
- Depop Extras: Employees enjoy free shipping on their Depop sales within the UK; Special milestones are celebrated with gifts and rewards
Machine Learning Scientist - Recommendations in London employer: Depop
Contact Detail:
Depop Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Machine Learning Scientist - Recommendations in London
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, attend meetups, and connect with Depop employees on LinkedIn. A friendly chat can sometimes lead to opportunities that aren’t even advertised!
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your machine learning projects, especially those related to recommendations. This gives you a chance to demonstrate your expertise beyond just a CV.
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge and problem-solving skills. Practice explaining complex concepts in simple terms, as you'll need to communicate effectively with both techies and non-techies at Depop.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen. Plus, it shows you’re genuinely interested in joining the Depop team.
We think you need these skills to ace Machine Learning Scientist - Recommendations in London
Some tips for your application 🫡
Tailor Your CV: Make sure your CV is tailored to the Machine Learning Scientist role. Highlight your experience with recommendation systems, deep learning techniques, 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 team at Depop. Be sure to mention specific experiences that relate to the job description.
Showcase Your Projects: If you've worked on any interesting machine learning projects, make sure to include them in your application. We love seeing practical examples of your work, especially if they involve large-scale data or innovative solutions in recommendations.
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 keep track of your application status. Plus, we love seeing candidates who take the initiative to apply directly!
How to prepare for a job interview at Depop
✨Know Your Models
Make sure you’re well-versed in the latest machine learning models, especially those related to recommendations. Be ready to discuss your experience with frameworks like PyTorch or TensorFlow and how you've applied them to solve real-world problems.
✨Collaborate Like a Pro
Since this role involves working closely with Product, Insights, and Engineering teams, be prepared to showcase your collaborative skills. Share examples of how you've successfully worked with cross-functional teams and communicated complex ideas to non-technical stakeholders.
✨Experimentation is Key
Highlight your experience with A/B testing and experiment design. Discuss specific experiments you've conducted, what hypotheses you tested, and how the results influenced product development. This shows you understand the importance of data-driven decision-making.
✨Stay Current and Curious
Demonstrate your passion for continuous learning by discussing recent advancements in machine learning and recommendation systems. Mention any relevant courses, conferences, or research papers you've engaged with to show you're proactive about staying up-to-date.
