Aftersales Data Analyst

The Job Summary The Aftersales Data Analyst is responsible for analyzing aftersales-related data; unlocking the power of data to optimize aftersales operations and elevate customer experiences and leveraging his analytical skills and data expertise to translate complex information into actionable insights. Key Responsibilities / Accountabilities Sr # Technical / Job-Specific Competencies 1 Data Analysis & Insights • Develop insightful daily, weekly, and monthly reports on aftersales performance metrics, including appointment tracker data and productivity in accordance with defined KPIs. • Analyze vehicle purchase frequency trends, identifying patterns and correlations across different models and car types. • Perform exploratory data analysis to uncover hidden patterns, trends, and relationships within complex datasets. • Utilize advanced statistical methods and machine learning techniques to extract actionable insights from aftersales data, empowering informed decision-making. • Create compelling data visualizations, dashboards, and reports to effectively communicate findings to stakeholders at all levels. • Deliver clear, concise, and regular (weekly and monthly) analyses and reports to senior management, fostering data-driven decision-making. • Demonstrate proficiency in a range of technical skills, including Power BI, Advanced Excel, data dash boarding tools, SQL and Python. Utilize these skills to extract, manipulate, and analyze large datasets. • Compile and enter data from various sources, including databases and collaborating with subject matter experts (SMEs) to leverage their business knowledge and enrich data analysis. • Troubleshoot and validate data and reports, proactively identifying and resolving inconsistencies; Take ownership of ensuring data quality and finding solutions to emerging challenges. • Adapt quickly to changes in reporting procedures, ensuring all reports remain current, relevant, and aligned with evolving business needs. 2 Strategic Planning & Business Development Support • Provide credible support in strategic planning and business development. • Leverage data to plan and implement customer engagement initiatives that enhance customer satisfaction and loyalty. • Utilize data analysis to inform strategies that enhance Nissan's agility and scalability within the competitive automotive market. • Assist in the aftersales-related projects, including yearly model updates, database skill development initiatives for technicians, integration of new parts and labor operation procedures, and vehicle configuration management. • Analyze data to identify opportunities for automation, such as in labor time updates, to reduce idle time and streamline aftersales processes. • Collaborate with business leaders to define key performance indicators (KPIs) and metrics that align with strategic objectives for the aftersales department. • Build predictive models to anticipate market trends, customer preferences, and product demand for aftersales services and parts. • Define the appropriate business analysis techniques, tools, and resources required for each aftersales data analysis project. The Job Profile 1 Educational Qualifications Bachelor’s degree in Computer Science or Business Administration 2 Professional Certifications Google Data Analytics Professional Certification; CAAP – Certified Automotive Aftermarket Professional 3 Experience Minimum 3 years experience in data analysis preferably in the automotive industry 4 Languages Proficiency Fluency in English & Arabic languages

Minimum 3 years experience in data analysis preferably in the automotive industry. Educational Qualifications: Bachelor’s degree in Computer Science or Business Administration. Professional Certifications: Google Data Analytics Professional Certification; CAAP – Certified Automotive Aftermarket Professional. Languages: Fluency in English & Arabic.

Develop insightful daily, weekly, and monthly reports on aftersales performance metrics, including appointment tracker data and productivity in accordance with defined KPIs. Analyze vehicle purchase frequency trends, identifying patterns and correlations across different models and car types. Perform exploratory data analysis to uncover hidden patterns, trends, and relationships within complex datasets. Utilize advanced statistical methods and machine learning techniques to extract actionable insights from aftersales data, empowering informed decision-making. Create compelling data visualizations, dashboards, and reports to effectively communicate findings to stakeholders at all levels. Deliver clear, concise, and regular analyses and reports to senior management. Demonstrate proficiency in Power BI, Advanced Excel, data dashboarding tools, SQL and Python. Compile and enter data from various sources, including databases, and collaborate with SMEs to enrich data analysis. Troubleshoot and validate data and reports, ensuring data quality and resolving inconsistencies. Adapt reporting procedures to evolving business needs. Support strategic planning and business development using data; assist in aftersales projects, model updates, technician skill development initiatives, integration of new parts and labor operation procedures, and vehicle configuration management. Build predictive models to anticipate market trends and demand for aftersales services and parts. Define appropriate business analysis techniques, tools, and resources for each project.