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For Companies At a Glance
- Tasks: Build and optimise machine learning models that enhance customer experience.
- Company: Join a fast-growing consumer tech leader on the path to IPO.
- Benefits: Competitive salary, flexible working, and exposure to senior leadership.
- Why this job: Shape core products with data science in a high-impact role.
- Qualifications: 3+ years as a Data Scientist with strong Python and AWS skills.
- Other info: Work in a dynamic environment with ownership of the full ML lifecycle.
The predicted salary is between 80000 - 100000 £ per year.
WeDo is currently working with one of the fastest growing consumer technology businesses in its sector. The company has scaled at exceptional pace, is operating at significant customer volume, and is widely regarded as a category leader. With a clear roadmap toward IPO, this is a rare opportunity to join a business at a defining stage of its growth.
Salary: £80,000 to £100,000
Location: London based hybrid working - 3 days a week onsite
Work Type: 12 month fixed term contract
Data is fundamental to how the product operates and evolves. This role focuses on building and optimising advanced machine learning models that directly influence customer experience, with a strong emphasis on personalisation and product recommendation. You will be part of a highly capable data team working closely with product and engineering in an AWS-first environment.
Responsibilities
- Analyse large and complex datasets to solve high impact, customer facing product problems
- Design, build, train, and deploy machine learning models using AWS services, with Amazon SageMaker as the core ML platform
- Develop and productionise recommendation and personalisation systems used at scale
- Own the full model lifecycle including experimentation, validation, deployment, and monitoring
- Run A/B testing and controlled experiments to evaluate product changes
- Present insights, results, and modelling approaches clearly to senior stakeholders
- Write production quality code following best practices in version control and deployment
- Monitor, optimise, and retrain models in live AWS environments
- Work closely with product managers and engineers to translate data science into measurable business outcomes
Required Skills
- 3 or more years experience as a Data Scientist in a product or customer focused environment
- Strong Python and SQL skills
- Hands on, production experience using AWS for machine learning workloads
- Strong experience with Amazon SageMaker for model training, deployment, and monitoring
- Experience building and deploying machine learning models into live products
- Solid understanding of applied machine learning techniques and their real world limitations
- Experience running experiments and A/B testing
- Confident communicator able to influence non technical and senior stakeholders
- Experience using Git and collaborating in multi developer environments
- Strong awareness of data ethics, bias, privacy, and handling sensitive customer data
Desirable experience includes CI/CD pipelines, model performance monitoring, modern data modelling tools, BI platforms, and cloud based endpoint deployment.
This is an opportunity to work inside a genuinely high growth business on a clear path to IPO, where data science directly shapes the core product. You will be working on problems at real scale, using modern AWS tooling, with ownership across the full machine learning lifecycle. The role offers exposure to senior leadership, meaningful technical influence, and the credibility of being part of a market defining company before it goes public. Combined with competitive compensation, flexible working, and a strong data culture, this is a compelling contract opportunity.
Interested? Apply for the role today or send your CV to pierre.rodriguez@wedotech.uk
Senior Data Scientist in London employer: WeDo
Contact Detail:
WeDo Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Senior Data Scientist in London
✨Tip Number 1
Network like a pro! Reach out to people in the industry, attend meetups, and connect on LinkedIn. You never know who might have the inside scoop on job openings or can refer you directly.
✨Tip Number 2
Prepare for interviews by practising common data science questions and case studies. We recommend doing mock interviews with friends or using online platforms to get comfortable with the format.
✨Tip Number 3
Showcase your projects! Create a portfolio that highlights your machine learning models and data analyses. This gives potential employers a tangible sense of your skills and creativity.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen. Plus, we love seeing candidates who are proactive about their job search.
We think you need these skills to ace Senior Data Scientist in London
Some tips for your application 🫡
Tailor Your CV: Make sure your CV reflects the skills and experiences that match the Senior Data Scientist role. Highlight your experience with AWS, machine learning models, and any relevant projects that showcase your ability to solve customer-focused problems.
Craft a Compelling Cover Letter: Use your cover letter to tell us why you're passionate about data science and how you can contribute to our growth. Mention specific examples of your work with personalisation systems or A/B testing to show you understand the impact of your role.
Showcase Your Communication Skills: Since you'll be presenting insights to senior stakeholders, it's crucial to demonstrate your ability to communicate complex ideas clearly. Use your application to highlight instances where you've successfully influenced non-technical audiences.
Apply Through Our Website: We encourage you to apply directly through our website for a smoother process. This way, we can easily track your application and ensure it gets the attention it deserves!
How to prepare for a job interview at WeDo
✨Know Your Data Inside Out
Make sure you’re well-versed in the datasets relevant to the role. Brush up on your experience with large and complex datasets, and be ready to discuss specific examples of how you've solved customer-facing product problems using data.
✨Showcase Your Machine Learning Mastery
Prepare to talk about your hands-on experience with AWS and Amazon SageMaker. Be ready to explain the full model lifecycle you've managed, including experimentation and deployment, and highlight any successful projects where your models made a significant impact.
✨Communicate Clearly and Confidently
Since you'll be presenting insights to senior stakeholders, practice explaining complex concepts in simple terms. Think of examples where you influenced non-technical audiences and how you made data-driven decisions that benefited the product.
✨Demonstrate Your Ethical Awareness
Be prepared to discuss data ethics, bias, and privacy. Show that you understand the importance of handling sensitive customer data responsibly and can articulate how you’ve navigated these challenges in your previous roles.
