Control Tower Analyst – Supply Chain

Role Overview

You will be the analytical backbone for our control tower operations, turning raw logistics data into clear, actionable insight that keeps inbound and outbound freight moving on schedule. Day to day, you will mine telematics and delivery datasets, build dashboards that give leadership a real-time view of performance, and work alongside the Data Processing Manager to sharpen how the wider team interprets numbers. Your work directly shapes client reporting and helps us spot trouble—like route deviations or missed delivery windows—before they become costly problems.

Key Responsibilities

• Own the production of recurring reports on inbound and outbound control tower activity, checking every figure for accuracy before they go to leadership or clients.
• Watch the KPIs that matter most—delivery conformance, turnaround time, order fill rates—and flag any metric slipping out of tolerance as soon as it does.
• Investigate operational warnings such as route deviations, delays, and non-conformance, then escalate findings with context so decision-makers can act quickly.
• Turn raw GPS and telematics feeds into clean, consolidated datasets that feed both daily reporting and longer-range analytics.
• Build interactive Power BI or Excel dashboards that let the project team monitor live performance and drill into exceptions without waiting on manual pulls.
• Partner with project leads to craft concise, client-ready summaries that explain performance trends and recommended improvements in plain language.
• Mentor junior analysts in statistical methods, data hygiene, and how to frame insights rather than just list numbers.
• Document every analysis workflow and reporting process so the work remains auditable and repeatable for future engagements.

Requirements & Qualifications

• Bachelor’s degree in statistics, data science, supply chain, or a closely related field; a master’s degree or equivalent post-graduate analytics training is a strong plus.
• At least two years of hands-on analytics experience, ideally inside logistics, freight, or supply chain operations.
• Working proficiency in Excel, SQL, and a visualization platform such as Power BI or Tableau; Python scripting for data manipulation is an advantage.
• Solid grasp of statistical modelling, forecasting methods, and how to choose the right visual for a given audience.
• Comfort moving between data-processing systems and reporting tools, including cleaning messy datasets and reconciling different sources.
• Sharp attention to detail, a structured approach to documentation, and the ability to explain technical findings to non-technical stakeholders.
• Problem-solving instincts that let you see both the operational risk and the opportunity hidden in a data anomaly.