Senior Data Analytics & BI, Global Converse

This role is responsible for delivering advanced analytics capabilities primarily supporting Supply Chain & Logistics, with additional partnership across Planning, Merchandising, Finance, and other enterprise functions. It blends business context, technical execution, and analytical expertise to develop forecasting, optimisation, and scalable analytical products using SQL, Python, machine learning, and modern analytics platforms. The role sits within the Data, Analytics & Intelligence (DAI) Team, which is at the centre of Converse's data and analytics ecosystem and enables business partners to make more informed decisions through trusted data, advanced analytics, and scalable BI capabilities.

• Develop, maintain, and evolve analytics products, decision-ready dashboards, KPI frameworks, and self-service analytics products that improve operational and strategic decision-making and enable Supply Chain & Logistics partners to monitor performance, investigate drivers, and take action.
• Access, analyse, and model Converse data using SQL, Python, and related analytical tools to generate actionable business insights.
• Translate complex or ambiguous business questions into structured, scalable analytics solutions that primarily support Supply Chain & Logistics, while also enabling Planning, Merchandising, Finance, and enterprise leadership needs.
• Build forward-looking analyses that identify trends, risks, opportunities, and potential business impacts across demand, inventory availability and productivity, order fulfilment, delivery performance, product, channel, and financial performance.
• Create reusable analytical workflows, models, and data products that improve speed, automation, consistency, and scalability of insight generation.
• Partner with business stakeholders to translate analytical outputs into clear, executive-ready recommendations that support growth, profitability, inventory productivity, and consumer outcomes.
• Improve data quality, accessibility, governance, and usability across the modern data stack, including the development and adoption of consistent KPI definitions and reusable semantic models.
• Contribute hands-on across querying, data transformation, feature engineering, statistical modelling, machine learning, and advanced analytics workflows.
• Advance data literacy and foster a stronger data science community within Converse through partnership, knowledge sharing, and practical application of modern analytical methods.
• Explore and apply emerging AI capabilities where they can responsibly accelerate analysis, improve signal quality, or scale decision support.

• Strong technical foundation in data manipulation, statistical modelling, forecasting, optimisation, and large-scale data analysis.
• Significant experience applying advanced analytical methods such as regression analysis, time-series modelling, cohort analysis, machine learning, or optimisation techniques.
• Ability to design, evaluate, and explain models that surface patterns, forecast outcomes, quantify risk, and identify business opportunities.
• Proficiency in SQL and Python, with experience building reusable analytical workflows and scalable solutions that move beyond one-off analysis.
• Experience working with imperfect or incomplete data and building structured approaches to improve signal quality, reliability, and interpretability.
• Ability to frame ambiguous business problems into analytically tractable approaches and communicate assumptions, tradeoffs, and implications clearly.
• Strong communication skills, with the ability to translate technical methodologies and results into clear recommendations for non-technical stakeholders.
• Experience partnering cross-functionally and balancing priorities across multiple stakeholder groups in a dynamic business environment.
• Curious, adaptable, and creative problem-solving mindset with strong attention to detail and accountability for delivery.
• 5+ years of experience in advanced analytics, data science, business analytics, BI, or related technical roles.
• Proficient in Python development, including pandas, scikit-learn, statistical modelling, forecasting, and optimisation techniques.
• Advanced SQL skills, including complex querying, data transformation, analytical modelling, and exposure to data engineering concepts.
• Familiarity with cloud analytics environments and modern data platforms (including Databricks and Snowflake).
• Practical experience applying AI/ML methods, particularly forecasting, predictive modelling, and optimisation.
• Experience rapidly prototyping analytical solutions and scaling successful approaches into repeatable products or production-ready outputs.

• Retail, consumer, or planning experience.
• Experience applying analytics within Supply Chain, Logistics, inventory management, order management, fulfilment, or related retail operations.
• Exposure to Generative AI, LLM-based workflows, agentic systems, or Retrieval-Augmented Generation.
• Experience with Sigma.
• Solid understanding of planning concepts such as demand, inventory, seasonality, performance management, and business forecasting.