Head of Marketing Data Science and Analytics

Head of Marketing Data Science and Analytics

Full-Time 56250 - 68750 Β£ / year (est.) Home office (partial)
StoneX

At a Glance

  • Tasks: Lead Marketing Analytics to transform data into strategic insights for growth.
  • Company: Join a Fortune 100 company connecting clients to global markets.
  • Benefits: Hybrid work model, competitive salary, and opportunities for professional development.
  • Other info: Dynamic environment with strong potential for career progression.
  • Why this job: Make a real impact by driving marketing performance with cutting-edge analytics.
  • Qualifications: 10+ years in Marketing Analytics with leadership experience required.

The predicted salary is between 56250 - 68750 Β£ per year.

Connecting clients to markets – and talent to opportunity. With 5,400+ employees and over 80,000 institutional, commercial, and payments clients, we operate from more than 80 offices spread across six continents. As a Fortune 100, Nasdaq-listed provider, we connect clients to the global markets – focusing on innovation, human connection, and providing world-class products and services to all types of investors.

Business Segment Overview: Engage in a deep variety of business-critical activities that keep our company running efficiently. From strategic marketing and financial management to human resources and operational oversight, you’ll have the opportunity to optimize processes and implement game-changing policies.

Responsibilities

Position Purpose: This role leads the Marketing Analytics function as a strategic business partner to the CMO and the wider Marketing Leadership Team. The function exists to transform raw data across paid media, owned channels, CRM and third-party signals into decisions that drive measurable growth. Sitting at the intersection of Analytics, AdTech and Marketing Strategy, this role defines the analytical capabilities required to improve marketing performance, translates complex business and marketing questions into clear analytical requirements, and directs analytics and data science teams to deliver actionable insights.

Primary duties will include:

  • Define and own the Marketing Analytics roadmap, prioritising initiatives that directly impact marketing strategy, budget allocation and commercial outcomes.
  • Establish measurement and insight frameworks across channels, campaigns and customer segments.
  • Own the marketing measurement strategy, including Brand Tracking, Marketing Mix Modelling (MMM), Multi-Touch Attribution, Incrementality Testing, Funnel Conversion and Customer Journey Analytics.
  • Define KPIs and reporting standards across Marketing, CRM and Lifecycle programmes.
  • Deliver forward-looking insights, performance narratives and strategic recommendations to Marketing and business leadership.
  • Translate business questions into analytical specifications, defining required inputs, outputs, methodologies and success criteria.
  • Act as the primary Marketing stakeholder for Data, Engineering and AdTech teams, ensuring analytical solutions meet business requirements.
  • Partner with Commercial, Product, Web, AdTech and Data teams to ensure accurate measurement, tracking integrity and data quality across all marketing activities.
  • Present analytical findings and recommendations to senior stakeholders in a clear, commercial and actionable manner.

Qualifications

To land this role you will need:

  • 10+ years' experience in Marketing Analytics, Marketing Science or quantitative marketing roles, with at least 5 years in a leadership capacity.
  • Demonstrated experience translating marketing and commercial objectives into analytics programmes that influence business strategy.
  • Proven track record leading analytics or data science teams and driving insight-led decision making.
  • Strong expertise in AI-driven marketing optimisation, advanced modelling and machine learning applications within marketing environments.
  • Deep understanding of Marketing Mix Modelling, attribution, incrementality testing, customer lifetime value and audience segmentation.
  • Experience operating in complex, multi-channel organisations; financial services or regulated industries preferred.
  • Strong stakeholder management, communication and influencing skills, with the ability to engage both technical and non-technical audiences.
  • Working proficiency in SQL, Python or R sufficient to validate analytical outputs and engage effectively with data science teams.
  • Experience with BI and visualisation platforms such as Power BI or Tableau.
  • Understanding of cloud-based data platforms, data pipelines and ETL processes.
  • Familiarity with digital advertising measurement, tag management, consent management and first-party data strategies.
  • Experience with marketing and analytics platforms including Google Tag Manager, Google Ads, DV360, SA360, Meta Business Suite, LinkedIn Campaign Manager, Salesforce or Microsoft Dynamics.

Education / Certification Requirements:

  • Master's degree in Actuarial Science, Statistics, Economics, Data Science, Mathematics or a related quantitative discipline.
  • Professional certification in Marketing Analytics, Digital Marketing or Data Strategy is an advantage.

Working environment: Hybrid role, 4 days in-office.

Head of Marketing Data Science and Analytics employer: StoneX

At StoneX Group, we pride ourselves on being an exceptional employer, offering a dynamic work environment that fosters innovation and collaboration. With a strong focus on employee growth, our EMEA office provides unique opportunities for professional development in the financial services sector, supported by a culture that values diversity and inclusion. Join us to be part of a forward-thinking team dedicated to making a meaningful impact in Financial Crime Prevention while enjoying the benefits of a Fortune 100 company.

StoneX

Contact Details:

StoneX Recruitment Team

We think you need these skills to ace Head of Marketing Data Science and Analytics

Marketing Analytics
Data Science
AI-driven Marketing Optimisation
Advanced Modelling
Machine Learning Applications
Marketing Mix Modelling
Attribution