Mopar is seeking a business-minded Aftersales Business Analytics & Insights Analyst to deliver advanced analytics and financial insights for after-sales performance. This role focuses on building and maintaining KPI frameworks, applying statistical and causal methods to deep-dive performance diagnostics (forecast vs. actual, variance decomposition, and driver/elasticity analysis), and developing time-series forecasting, scenario, and optimization models that inform pricing, revenue, margin, and cost actions. The ideal candidate blends strong analytical and statistical skills with a solid finance foundation (e.g., FP&A and financial modeling) and can translate complex findings into clear recommendations for leadership.

Key Responsibilities:
Data & KPI Foundations:

• Partner with Finance, Commercial, and IT teams to source the right data for analysis, ensuring clear definitions and alignment on business rules.
• Establish and maintain KPI definitions, metric logic, and documentation to support consistent reporting and decision-making across stakeholders.
• Perform data validation and reconciliation (e.g., source-to-report checks and KPI tie-outs) to improve accuracy, trust, and auditability of analytics outputs.
• Support reporting and forecasting cycles by translating requirements into repeatable analyses and dashboards; escalate data gaps and collaborate on fixes with data/platform owners as needed.

Advanced Analytics & Data Science:

• Analyze complex datasets to uncover trends, drivers, and actionable insights across revenue, margin, cost, and operational KPIs; communicate findings through clear narratives and executive-ready outputs.
• Build, train, and deploy predictive models for demand and performance forecasting and promotion effectiveness, churn/segmentation, and anomaly detection (e.g., warranty, claims, or spend outliers).
• Conduct A/B testing, causal inference, and statistical analysis to evaluate initiatives; quantify ROI, uplift, and financial impact; and recommend actions based on measurable outcomes.
• Apply best practices in data visualization to ensure clarity, accuracy, and accessibility of insights, including interactive dashboards and automated reporting for forecast vs. actual, variance analysis, and operational performance.

Collaboration & Stakeholder Engagement:

• Serve as a technical liaison and finance analytics partner, translating business questions into scalable data solutions and decision frameworks (KPIs, driver trees, and operating rhythms).
• Educate and mentor team members on data and finance analytics best practices, including metric definitions, data governance, experimentation, and emerging technologies.