At a Glance
- Tasks: Lead AI-driven personalisation projects and develop cutting-edge recommendation systems.
- Company: Global lifestyle brand focused on consumer intelligence and engagement.
- Benefits: Competitive salary, hybrid work model, and opportunities for professional growth.
- Other info: Join a collaborative environment with excellent career advancement potential.
- Why this job: Make a real impact in a dynamic team using advanced machine learning techniques.
- Qualifications: Experience in machine learning, particularly with recommendation systems and deep learning.
The predicted salary is between 70000 - 80000 £ per year.
A global lifestyle brand is hiring a Data Science Manager to lead personalisation efforts within its CRM ecosystem. The role sits in the Consumer Intelligence and Experience (CIX) team, which drives customer engagement through predictive analytics and insights across all brands and channels. You'll develop recommendation systems and predictive models that support global marketing and CRM strategies.
Responsibilities
- Lead development of machine learning solutions for CRM personalisation
- Build and optimise recommendation engines using neural networks and deep learning
- Collaborate with CRM and regional marketing teams to align with campaign goals and segmentation strategies
- Partner with engineering and data teams to ensure scalable solutions
- Monitor and improve model performance using data insights and feedback
Requirements
- 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 and ML libraries (e.g. pandas, numpy, scipy, scikit-learn, tensorflow, pytorch)
- Familiarity with cloud platforms (GCP, AWS, Azure) and tools like Dataiku
- Experience with ML Ops, including 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
Details
- Salary: £70-80k per annum
- Duration: Permanent
- Location: Hybrid, with 2-3 days/week in Central London office
- Seniority level: Mid-Senior level
- Employment type: Full-time
- Job function: Information Technology
- Industries: Data Infrastructure and Analytics
Data Science Manager employer: Freshminds
Join a top-tier consultancy in London, where you will thrive in a dynamic work culture that values innovation and collaboration. With a strong focus on employee growth, we offer extensive training and development opportunities, ensuring you can advance your career while working on impactful projects in the manufacturing sector. Enjoy the unique advantage of being part of a leading operations practice, where your contributions directly influence client success and operational excellence.
StudySmarter Expert Advice🤫
We think this is how you could land Data Science Manager
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, especially those already working in data science roles. A friendly chat can lead to insider info about job openings or even referrals.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your machine learning projects, especially those involving recommendation systems. 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. Be ready to discuss your experience with Python, ML libraries, and how you've collaborated with cross-functional teams in the past.
✨Tip Number 4
Don't forget to apply through our website! We’ve got loads of opportunities waiting for talented individuals like you. Plus, it’s a great way to stay updated on new roles that match your skills.
We think you need these skills to ace Data Science Manager
Some tips for your application 🫡
Tailor Your CV:Make sure your CV highlights your experience in machine learning and recommendation systems. We want to see how your skills align with the role, so don’t be shy about showcasing relevant projects or achievements!
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Use it to explain why you’re passionate about personalisation in CRM and how your background makes you the perfect fit for our CIX team. Keep it engaging and to the point!
Showcase Your Technical Skills:Since this role involves a lot of technical know-how, make sure to mention your proficiency in Python and any ML libraries you’ve worked with. We love seeing practical examples of your work, so feel free to include links to your GitHub or projects.
Apply Through Our Website:We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you don’t miss out on any important updates. Plus, we can’t wait to hear from you!
How to prepare for a job interview at Freshminds
✨Know Your Machine Learning 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 Collaboration Skills
This role involves working closely with CRM and marketing teams, so be prepared to share examples of how you've successfully collaborated in the past. Highlight any cross-functional projects you've been part of and how you aligned technical solutions with business goals.
✨Demonstrate Your Technical Proficiency
Familiarise yourself with the tools and libraries mentioned in the job description, like Python, pandas, and TensorFlow. You might be asked to solve a technical problem or discuss your approach to building and optimising models, so be ready to dive into the details.
✨Prepare for Real-World Scenarios
Think about how you would apply your skills in a global context. Prepare to discuss how you would monitor and improve model performance using data insights, and be ready to talk about any experience you have with ML Ops and deployment strategies.
