Data Scientist

VML MAP
London
1 month ago
Applications closed

Related Jobs

View all jobs

Data Scientist

Data Scientist

Data Scientist

Data Scientist

Data Scientist (Government)

Data Scientist Placement

Overview

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 who build 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.
  • 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: Comfortable working with Data Engineers on deployment, familiar with scoring pipelines, feature engineering workflows, and orchestration tools (Airflow, Terraform).
  • Good engineering practices: Comfortable 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 (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: Comfortable working in cross‑functional teams and partnering closely with engineers, strategists, and client stakeholders.
Equal Opportunity and Disability Self‑Identification

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.

You are welcome to complete our voluntary disability self‑identification form. The form is confidential and not used in hiring decisions. It is voluntary and you have the right to decline.


#J-18808-Ljbffr

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

How Many Data Science Tools Do You Need to Know to Get a Data Science Job?

If you’re trying to break into data science — or progress your career — it can feel like you are drowning in names: Python, R, TensorFlow, PyTorch, SQL, Spark, AWS, Scikit-learn, Jupyter, Tableau, Power BI…the list just keeps going. With every job advert listing a different combination of tools, many applicants fall into a trap: they try to learn everything. The result? Long tool lists that sound impressive — but little depth to back them up. Here’s the straight-talk version most hiring managers won’t explicitly tell you: 👉 You don’t need to know every data science tool to get hired. 👉 You need to know the right ones — deeply — and know how to use them to solve real problems. Tools matter, but only in service of outcomes. So how many data science tools do you actually need to know to get a job? For most job seekers, the answer is not “27” — it’s more like 8–12, thoughtfully chosen and well understood. This guide explains what employers really value, which tools are core, which are role-specific, and how to focus your toolbox so your CV and interviews shine.

What Hiring Managers Look for First in Data Science Job Applications (UK Guide)

If you’re applying for data science roles in the UK, it’s crucial to understand what hiring managers focus on before they dive into your full CV. In competitive markets, recruiters and hiring managers often make their first decisions in the first 10–20 seconds of scanning an application — and in data science, there are specific signals they look for first. Data science isn’t just about coding or statistics — it’s about producing insights, shipping models, collaborating with teams, and solving real business problems. This guide helps you understand exactly what hiring managers look for first in data science applications — and how to structure your CV, portfolio and cover letter so you leap to the top of the shortlist.

The Skills Gap in Data Science Jobs: What Universities Aren’t Teaching

Data science has become one of the most visible and sought-after careers in the UK technology market. From financial services and retail to healthcare, media, government and sport, organisations increasingly rely on data scientists to extract insight, guide decisions and build predictive models. Universities have responded quickly. Degrees in data science, analytics and artificial intelligence have expanded rapidly, and many computer science courses now include data-focused pathways. And yet, despite the volume of graduates entering the market, employers across the UK consistently report the same problem: Many data science candidates are not job-ready. Vacancies remain open. Hiring processes drag on. Candidates with impressive academic backgrounds fail interviews or struggle once hired. The issue is not intelligence or effort. It is a persistent skills gap between university education and real-world data science roles. This article explores that gap in depth: what universities teach well, what they often miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in data science.