Manager-Data & Analytics

Role & responsibilities

• Roadmap and prioritisation: Turn GAVL and Business Unit priorities into a clear analytics roadmap, ranked by value, feasibility and data readiness, with a business owner for each use case.
• Business engagement: Work with sales, manufacturing, supply chain, procurement, finance and other functions to define the decision to be improved, agree baselines and success measures, and pick the right solution, whether a report, forecast, model, alert or self-service tool. Manage dependencies with IT and external partners.
• Team leadership and review: Set priorities and standards for Data Engineers, BI Developers and Data Scientists. Review designs, data models, reports and models at key stages, coach the team and keep ownership clear.
• Analytical rigour: Question assumptions, check methods and interpret results so recommendations are statistically sound and clearly explained. Make sure models are explainable and monitored for drift.
• Data governance: Keep metric definitions, data quality, documentation, access and security consistent, working with data owners, the Data Platform and Governance team, and IT.
• Adoption and value: Plan adoption for each major product with business champions, training and feedback. Track usage and decision impact, improve or retire low-use outputs, and confirm benefits with business owners.
• Platform and cost: Know GAVL's data landscape well enough to guide design, reuse and cost together with the platform team.

Technical and Functional Capabilities

Data science and analytical review

• Working knowledge of hypothesis testing, sampling, bias, confidence intervals and correlation versus causation.
• Understanding of regression, classification, clustering, forecasting, model validation and drift, enough to review the team's work and explain results to the business.

Data, BI and platform landscape

• Understanding of Azure Databricks, Delta Lake, Azure Data Factory, Unity Catalog, Databricks Genie, MLflow and Power BI.
• Good SQL; ability to read Python or PySpark is an advantage.
• Familiarity with data quality, lineage, access control and CI/CD practices.

Business and leadership capabilities

• Turns loosely defined business problems into clear analytical questions and explains findings plainly to senior and non-technical audiences.
• Influences without authority and builds lasting adoption across business teams.
• Exposure to agri-business, manufacturing, supply chain, sales or finance data is desirable.

Preferred candidate profile