At a Glance
- Tasks: Transform business challenges into data-driven solutions using machine learning and analytics.
- Company: Join a leading tech company focused on hyper-personalisation and customer-centric strategies.
- Benefits: Competitive salary, diverse team, and opportunities for professional growth.
- Why this job: Make a real impact by delivering predictive models for world-renowned brands.
- Qualifications: Experience in data science, machine learning, and strong statistical skills required.
- Other info: Collaborative environment with a global network of over 30,000 professionals.
The predicted salary is between 36000 - 60000 £ per year.
We are seeking a Data Scientist to join our Data & AI practice. This is a role for a pragmatic problem-solver who can translate business challenges into data-driven solutions for world-renowned brands. You will apply statistical thinking and machine learning techniques to deliver predictive capabilities that drive measurable marketing and customer experience outcomes—and you’ll be comfortable taking your models beyond the notebook into production environments.
What will your day look like? This is a hands-on role where you will deliver impactful analytics and machine learning solutions, primarily leveraging out-of-the-box platform capabilities while applying solid statistical foundations to ensure rigorous, trustworthy results. More specifically, your tasks will include:
- Developing and deploying predictive models (propensity, churn, lookalike) and recommendation systems for targeted campaigns and personalization.
- Conducting deep customer analytics (segmentation, LTV, behavioral analysis) to generate actionable insights.
- Implementing ML solutions using both platform-native capabilities and custom development, ensuring models are production-ready and AI-consumable.
- Maintaining statistical rigor in all methodologies, from experiment design to model validation, supported by robust data exploration and preparation.
- Advising clients on data science opportunities, communicating complex findings clearly, contributing to repeatable solution frameworks, and fostering cross-functional collaboration with engineering, strategy, and client services teams.
Who are you going to work with? You will join a team of Data Scientists and Analysts who are passionate about turning data into business impact. You’ll work closely with our Data Engineering team that builds the pipelines and infrastructure that power your models—and at times, you’ll contribute directly to that work. Beyond your immediate team, you will collaborate with stakeholders across our organization (strategy leads, account directors, creative teams) and directly with client marketing and analytics teams.
What do you bring to the table? You are a practical, business-minded data scientist who prioritizes delivering value over theoretical perfection. You have strong statistical intuition and can clearly explain analytical approaches and their limitations to both technical and non-technical audiences. You’re not just a notebook data scientist—you understand what it takes to get models into production.
- Solid statistical foundation: Strong understanding of inferential statistics, hypothesis testing, regression analysis, and experimental design. You don’t need to derive algorithms from scratch, but you must understand when and why to apply them.
- Applied machine learning experience: Hands-on experience building propensity models, lookalike/similarity models, customer segmentation, churn prediction, lifetime value models, and recommendation systems.
- Proficiency in Python and SQL: For data manipulation, analysis, and model development.
- Production-aware mindset: Understanding of what it takes to move models from experimentation to production. You’re comfortable working with Data Engineers on deployment, and familiar with concepts like scoring pipelines, feature engineering workflows, and orchestration tools (e.g., Airflow, Terraform).
- Good engineering practices: Comfort with version control (Git), writing clean and maintainable code, and collaborating in shared codebases.
- Experience with cloud ML platforms: Familiarity with cloud-based ML services (e.g., GCP Vertex AI) and/or marketing platform ML capabilities (Salesforce Einstein, Adobe Sensei).
- Data exploration skills: Ability to use visualization tools to inspect data, validate assumptions, and inform modeling decisions.
- Business acumen: Ability to connect analytical work to business outcomes and communicate value in terms clients care about. Agency or consulting experience is a strong advantage.
- Collaborative mindset: Comfort working in cross-functional teams and partnering closely with engineers, strategists, and client stakeholders.
A leader in personalized customer experiences VML MAP is a world-leading Centre of Excellence that helps businesses humanize the relationship between the brand and the customer through hyper personalisation at scale, marketing automation and CRM. With the brain of a consultancy, the heart of an agency and the power of technology and data, we work with some of the world’s most admired brands to help them on their transformation journey to becoming truly customer-centric.
Together, we are 1000+ technology specialists, data scientists, strategic thinkers, consultants, operations experts, and creative minds from 55+ nationalities. A global network We are part of the global VML network that encompasses more than 30,000 employees across 150+ offices in 60+ markets, each contributing to a culture that values connection, belonging, and the power of differences.
WPP (VML MAP) is an equal opportunity employer and considers applicants for all positions without discrimination or regard to characteristics. We are committed to fostering a culture of respect in which everyone feels they belong and has the same opportunities to progress in their careers.
Data Scientist employer: Wunderman
Contact Detail:
Wunderman Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Data Scientist
✨Tip Number 1
Network like a pro! Reach out to current employees at the company you're eyeing, especially those in data roles. A friendly chat can give you insider info and might just get your foot in the door.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your best projects, especially those involving predictive models or customer analytics. This is your chance to demonstrate how you can turn data into actionable insights.
✨Tip Number 3
Prepare for the interview by brushing up on your statistical foundations and machine learning techniques. Be ready to discuss how you've applied these in real-world scenarios—employers love practical examples!
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, it shows you’re genuinely interested in joining our team.
We think you need these skills to ace Data Scientist
Some tips for your application 🫡
Tailor Your CV: Make sure your CV reflects the skills and experiences that align with the Data Scientist role. Highlight your applied machine learning experience and any relevant projects that showcase your ability to deliver data-driven solutions.
Craft a Compelling Cover Letter: Use your cover letter to tell us why you're the perfect fit for this role. Share specific examples of how you've tackled business challenges with data, and don't forget to mention your collaborative mindset and experience working in cross-functional teams.
Showcase Your Technical Skills: Be sure to include your proficiency in Python and SQL, as well as any experience with cloud ML platforms. We want to see that you’re not just familiar with these tools but can also apply them effectively in real-world scenarios.
Apply Through Our Website: We encourage you to submit your application through our website. This way, we can ensure your application gets the attention it deserves, and you’ll be one step closer to joining our passionate team of Data Scientists!
How to prepare for a job interview at Wunderman
✨Know Your Stats
Brush up on your inferential statistics, hypothesis testing, and regression analysis. Be ready to discuss how you've applied these concepts in real-world scenarios, especially in building predictive models or conducting customer analytics.
✨Showcase Your Practical Experience
Prepare to share specific examples of machine learning projects you've worked on. Highlight your hands-on experience with propensity models, churn prediction, and recommendation systems, and be ready to explain how you moved these models from experimentation to production.
✨Communicate Clearly
Practice explaining complex analytical concepts in simple terms. You’ll need to convey your findings to both technical and non-technical audiences, so think about how you can make your insights relatable and actionable for clients.
✨Collaborate Like a Pro
Demonstrate your ability to work in cross-functional teams. Be prepared to discuss how you've partnered with data engineers or strategists in the past, and show that you understand the importance of collaboration in delivering impactful data solutions.