Find a job 
Featured Companies 
Featured Universities 
For Companies At a Glance
- Tasks: Lead the development of machine learning solutions for CRM personalisation and optimise recommendation engines.
- Company: Join a leading recruitment agency in analytics with a focus on innovation.
- Benefits: Competitive salary, hybrid working, and opportunities for professional growth.
- Why this job: Make an impact by using advanced tech to enhance customer experiences.
- Qualifications: Proven experience in machine learning and strong leadership skills required.
- Other info: Collaborative environment with excellent career advancement opportunities.
The predicted salary is between 60000 - 85000 £ per year.
Salaries in the region of £70,000 - £85,000 DoE.
Hybrid working – 2/3 days central London office.
Full UK working rights required / no sponsorship available.
Immediate requirement – strong leadership skills.
The role
- Lead development of machine learning solutions for CRM personalization.
- Build and optimize recommendation engines using neural networks and deep learning, incorporating product embeddings and other advanced features to improve relevance and performance.
- Collaborate with CRM and regional marketing teams to align with campaign goals and customer segmentation strategies.
- Own the full ML lifecycle – from model design to deployment and monitoring.
- Partner with engineering and data teams to ensure scalable solutions.
- Continuously monitor and improve model performance using data insights and feedback.
Skills
- Proven experience in machine learning, particularly in recommendation systems and deep learning architectures.
- Strong understanding of two‑tower neural networks, embedding techniques, and ranking models.
- Proficiency in Python with familiarity to ML libraries such as pandas, numpy, scipy, scikit‑learn, TensorFlow, PyTorch.
- Familiarity with cloud platforms (GCP, AWS, Azure) and tools like Dataiku.
- Experience with ML Ops, including model deployment, monitoring, and retraining pipelines.
- Ability to work cross‑functionally with marketing, CRM, and engineering teams.
- Excellent communication and stakeholder management skills.
- Experience in a global or multi‑regional context is a plus.
If you would like to hear more, please do get in touch.
Alternatively, you can refer a friend or colleague by taking part in our fantastic referral schemes. If you have a friend or colleague who would be interested in this role, please refer them to us. For each relevant candidate that you introduce to us (there is no limit) and we place, you will be entitled to our general gift/voucher scheme.
Datatech is one of the UK's leading recruitment agencies in the field of analytics and hosts the critically acclaimed event, Women in Data.
Senior Data Scientist in London employer: Datatech Analytics
Contact Detail:
Datatech Analytics 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 your industry on LinkedIn or at events. We all know that sometimes it’s not just what you know, but who you know that can help you land that Senior Data Scientist role.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your machine learning projects, especially those involving recommendation systems and deep learning. This will give potential employers a taste of what you can do and set you apart from the crowd.
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge and soft skills. We recommend practising common interview questions related to ML lifecycle and stakeholder management. Confidence is key, so get ready to shine!
✨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 hearing from passionate candidates like you who are eager to make an impact in the data science world.
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 is tailored to the Senior Data Scientist role. Highlight your experience with machine learning, recommendation 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 this role and how your background makes you a perfect fit. Don’t forget to mention your leadership skills and experience working cross-functionally.
Showcase Your Technical Skills: Be sure to list your proficiency in Python and any ML libraries you’ve used, like TensorFlow or PyTorch. We love seeing candidates who can demonstrate their technical expertise, especially in areas like deep learning and model deployment.
Apply Through Our Website: We encourage you to apply through our website for the best chance of getting noticed. It’s super easy, and we’ll be able to review your application more efficiently. Plus, you’ll be one step closer to joining our awesome team!
How to prepare for a job interview at Datatech Analytics
✨Know Your ML Stuff
Make sure you brush up on your machine learning knowledge, especially around recommendation systems and deep learning architectures. Be ready to discuss your experience with two-tower neural networks and embedding techniques, as these are crucial for the role.
✨Showcase Your Leadership Skills
Since strong leadership skills are a must-have, think of examples where you've led projects or teams. Prepare to share how you’ve collaborated with cross-functional teams, particularly in marketing and engineering, to achieve common goals.
✨Demonstrate Your Technical Proficiency
Be prepared to talk about your proficiency in Python and your experience with ML libraries like TensorFlow and PyTorch. You might even want to bring up specific projects where you deployed models or worked with cloud platforms like GCP or AWS.
✨Communicate Effectively
Excellent communication is key, so practice explaining complex technical concepts in simple terms. Think about how you can convey your insights and feedback effectively, especially when discussing model performance with stakeholders.
