Senior Manager - Data Science (Martech/CRM)

VML group; Greater London; On-site; GBP 90,000 - 120,000; Full time

Job summary

VML group in Greater London is seeking a Senior Manager - Data Science to lead a team of data scientists and enhance data-driven marketing solutions. You will manage the entire data science practice, ensuring high-quality deliverables and maintaining strong client relationships.

The ideal candidate has 5–8 years of experience in data science, excellent technical skills, and a proven ability to manage teams. Strong expertise in statistical analysis and familiarity with CRM platforms are required.

Qualifications

• 5–8 years of hands-on data science experience combining technical depth with client advisory.
• Strong expertise in statistical analysis and ML techniques.
• Demonstrated experience managing a team with performance reviews and mentoring.

Responsibilities

• Lead a team of data scientists, managing hiring and personal development.
• Contribute to designing and shipping ML models and pipelines.
• Partner with clients and strategy teams for data-driven decision making.

Skills

Statistical analysis

Machine learning techniques

Python

Advanced SQL

CRM knowledge

Cloud platform experience

Tools

Adobe AEP

Salesforce Data Cloud

Braze

Job description

Senior Manager - Data Science (Martech/CRM)

About WPP

WPP is the trusted growth partner for the world’s leading brands. We unite cutting‑edge media intelligence and data solutions, world‑class creativity, next‑generation production, transformative enterprise solutions and expert strategic counsel in a single company – powered by exceptional talent and our agentic marketing platform, WPP Open, to help our clients navigate change, capture opportunity and deliver transformational growth. For more information, visit WPP.com.

We're looking for a Lead Data Scientist and manager of the practice to lead this next chapter. You'll own the data science practice end‑to‑end; building and managing the team, advising clients on what's possible, and delivering work that raises the bar for what data can do within marketing ecosystems.

Client demand is growing. And you'll have real autonomy to shape what this practice becomes: the product offering, the ways of working, and how data science becomes an accelerator that elevates the work of activation, engineering, and insights teams around you.

• Lead & Grow the Team: Manage a team of data scientists end‑to‑end; hiring, onboarding, personal development, performance conversations, and day‑to‑day support. Coach team members technically and professionally.
  Identify skill gaps, plan for future needs, and scale the team as the practice grows. Foster a culture of knowledge sharing, curiosity, and quality.
• Build, Not Just Oversee: Personally contribute to high‑complexity initiatives; designing and shipping ML models, training and inference pipelines, and experimentation frameworks. Bring deep, hands‑on expertise in marketing‑relevant techniques: uplift modelling, propensity scoring, attribution, next best action modelling, segmentation, causal inference, and recommender systems. Set the technical bar through code and craft, not just review.
• Advise Clients Strategically: Partner with account and strategy teams on scoping, proposals, and commercial conversations: you're a credible voice in the room when clients are deciding where to invest.
  Act as a trusted data advisor. Proactively identify gaps and untapped opportunities in clients' data and marketing ecosystems. Translate complex findings into business recommendations.
  Anticipate business needs and challenges; build lasting client relationships as the go‑to person for data science matters.
• Deliver End‑to‑End with Quality: Lead complex data science initiatives from hypothesis and experimentation through model development, validation, and activation. Collaborate closely with Data Engineers and Architects so solutions are grounded in solid infrastructure. Own quality across all team deliverables, with discipline in statistical methods, reproducibility, and insight generation.
• Lead the team into Agentic AI: Champion agentic AI as both a way of working and a delivery capability. Coach data scientists to adopt AI‑native engineering practices (agentic coding, AI‑assisted development, prompt and context engineering) and lead the design of agentic solutions for clients: from internal accelerators to production‑grade agents embedded in marketing workflows. We expect you have shipped or prototyped agentic solutions and have a clear point of view on where they create real value.
• Apply CRM & Marketing Domain Expertise: Contextualize data science within clients' CRM, campaign, and personalization strategies. Leverage hands‑on experience with platforms like Adobe AEP, Salesforce Data Cloud, or Braze to extend marketing measurement and influence communications from a data perspective.
• Shape the Practice: Establish guidelines, ways of working, and reusable accelerators. Maintain a backlog of data science use cases. Contribute to road‑mapping, maturity frameworks, and the data science product catalogue alongside the Global Data Practice Lead.

Who are you going to work with?

You will lead a small, technically capable team of data scientists who need a manager with both technical credibility and genuine investment in their growth. Beyond your team: Data Engineers, Architects, Strategy Consultants, Account teams, and clients directly.

What do you bring to the table?

Data Science & Technical

• 5–8 years of hands‑on data science experience combining technical depth with client advisory.
• Strong expertise in statistical analysis, experimentation, ML techniques (regression, classification, clustering, hypothesis testing, next best action modelling).
• Proficiency in Python and advanced SQL.
• Solid grasp of adjacent domains: data modelling, ETL/ELT strategy, no‑SQL databases, pipeline design—enough to collaborate credibly with engineers and architects.
• Domain knowledge in CRM and marketing technology: campaign metrics, measurement frameworks, and experience with platforms such as Adobe, Salesforce, or Braze.
• Cloud platform experience (GCP, AWS, or Azure).

Advisory & Business

• Consulting craft: run client conversations end‑to‑end — scoping problems, shaping proposals, writing SOWs, contributing to RFPs, and defending recommendations under challenge. Comfortable moving between boardroom narrative and technical detail in the same meeting. Prior consulting or agency experience expected.
• Commercial & advisory instinct: track record of identifying data‑driven opportunities, building maturity roadmaps, and translating data science into business outcomes. Sound judgement on effort, ROI, and prioritisation by impact.

People Management

• Demonstrated experience managing a team: mentoring, performance reviews, hiring, and day‑to‑day support. Formal management experience is essential.
• Coaching‑oriented leadership style. You grow people through trust, feedback, and investment—not top‑down direction.
• Comfort scaling a team that's still taking shape: anticipating needs, defining roles, onboarding effectively.
• Emotional intelligence, empathy, and conflict resolution skills.

Excellent communication skills in English.

Nice to Have

• Agency or consultancy experience in fast‑paced, multi‑client environments.
• CRM certifications (e.g., Adobe CJA, RTCDP).
• Familiarity with data governance and privacy (GDPR, CCPA).
• Experience with out‑of‑the‑box vendor intelligent services.
• Exposure to agentic AI or AI‑driven automation use cases.
• Management or leadership certifications.

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.