At a Glance
- Tasks: Lead global aftersales performance strategy and drive data-driven decisions.
- Company: Inchcape plc, a leader in automotive services with a focus on innovation.
- Benefits: Competitive salary, career development opportunities, and a dynamic work environment.
- Other info: Collaborative culture with opportunities for growth across multiple markets.
- Why this job: Shape the future of aftersales and make a real impact on customer satisfaction.
- Qualifications: Strong analytics background and experience in KPI reporting.
The predicted salary is between 60000 - 80000 £ per year.
Inchcape plc is seeking a senior analytics professional to shape the global aftersales performance strategy.
You will own KPI reporting, develop executive dashboards, and drive data-driven decisions across multiple markets to enhance service, parts revenue and customer outcomes.
You will collaborate with regional teams, optimise systems, and enable standardised playbooks that support growth and operational excellence across the organisation.
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Global Aftersales Performance & Ops Leader employer: Inchcape plc
As a global automotive distribution leader, we pride ourselves on fostering a dynamic work environment that encourages innovation and collaboration. Our commitment to employee growth is reflected in our comprehensive training programmes and leadership development opportunities, ensuring that you can advance your career while making a meaningful impact in the financial reporting landscape. Join us and be part of a team that values excellence, integrity, and the pursuit of high-quality results.
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We think this is how you could land Global Aftersales Performance & Ops Leader
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We think you need these skills to ace Global Aftersales Performance & Ops Leader
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
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