Head of Analytics at LEGO Group

Play your part in our team success:

Analytics & Insights Partnering roles act as strategic advisors, using data-driven insights to inform decisions, optimize performance, and drive commercial growth. They blend business acumen, analytical expertise, and storytelling skills to deliver actionable recommendations that address both short-term goals and long-term strategies.

Data Analytics & Business Intelligence work encompasses a range of activities aimed at extracting insights from raw data to help make informed decisions.

Key activities include:

• Analyzing business intelligence data to inform business and product decisions
• Data modelling and report building / optimization
• Blending historical data from available industry reports, public information, field reports or purchased sources as input to analyses
• Identifying and analyzing industry/geographic trends and competitor market strategies and monitoring current/potential customer trends
• Focusing on the technical aspects of data analysis, management, and reporting

Core Responsibilities:

• Business Performance Analysis: Conduct deep-dive analysis into business performance metrics, identifying trends, patterns, and outliers to uncover growth opportunities and potential risks.
• Advanced Analytics & Forecasting: Apply statistical, mathematical, and predictive modeling techniques to forecast market dynamics, assess economic conditions, and support scenario planning.
• Data Architecture & Models: Develop data frameworks, visualization dashboards, and analytical models that enable scalable insights and decision-making.
• Integrated Insights: Combine internal performance metrics with market, competitive, shopper, and macroeconomic data to deliver a comprehensive view of business health and identify areas for improvement.
• Strategic Storytelling: Translate complex data insights into clear, compelling narratives that influence partners and drive actionable decisions aligned with business objectives.
• Collaborative Partnerships: Work cross-functionally with marketing, sales, finance, and operations to align insights with business strategies and ensure data-informed execution.

Do you have what it takes?

• Analytical Proficiency: Expertise in SQL, Python, and visualization tools (e.g., Tableau, Power BI).
• Data Strategy & Modeling: Strong understanding of data platforms (e.g., Snowflake, Databricks) and ability to develop statistical models for scenario analysis and performance measurement.
• Problem-Solving Ability: Aptitude for breaking down complex problems and applying analytical frameworks to generate solutions.
• Storytelling with Data: Ability to create compelling narratives that align insights with business priorities and influence partners.
• Commercial Acumen: Deep understanding of business performance drivers, market trends, and consumer behavior to provide insights that impact commercial strategies.
• Collaboration & Influence: Skilled at partnering with cross-functional teams and influencing collaborators at all levels
• Oversee the entire analytics function within an organization, ensuring that the team's outputs align with business goals and strategies.
• Develop the long-term vision for the analytics function and how it should evolve in line with business objectives.
• Provide executive-level reports and strategic insights to guide the overall direction of the business.
• Lead and manage a team of analysts and partners across various business units.
• Collaborate closely with senior leadership, ensuring analytics solutions meet business needs and provide competitive advantage.
• Manage budgets and resources for the analytics team, ensuring the team is well-equipped to meet organizational goals."
• Lead a team
• Independently handle conflicts in negotiation
• Manage large projects or processes independently
• Work independently, recognized subject matter expert, able to lead others, tasks are independently identified and defined"

Minimum Qualifications:

• Expertise with Big Data Tools & Platforms: Strategically evaluates and optimizes data platform usage; sets standards for the team; may not implement directly but possesses deep understanding of tool functionalities.
• SQL and Python Coding: Proficient – Reviews code and provides guidance; focuses on leading technical direction rather than hands-on coding.
• Data Science Tools: Advanced – Understands broader applications; collaborates with technical teams to scale predictive and prescriptive analytics solutions.
• Exploratory Data Analysis & Statistical Methods: Advanced – Guides teams in designing experiments and deriving insights aligned with business strategy.
• Storytelling with Data & Commercial Insights: Expert – Communicates insights effectively to executive-level audiences; drives alignment between data insights and business decisions.