Aftersales Business Analytics Program Manager

Overview 

Mopar is seeking a business-minded Aftersales Business Analytics Manager to deliver advanced analytics and financial insights for the after-sales division. 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, coordinate, and mentor team members on data analytics best practices, including metric definitions, data governance, experimentation, and emerging technologies. 

Basic Qualifications 

• Bachelor’s or Master’s degree in Data Science, Computer Science, Industrial Engineering, Finance, Economics, Accounting, Statistics, or a related quantitative field. 

• 7+ years of experience in analytics, FP&A, pricing/revenue management, or data/finance analytics roles (automotive/after-sales experience preferred). 

• Strong ability to analyze data using SQL and/or Python (e.g., querying, cleaning, analysis, and basic automation). 

• Proficiency in financial modeling and analysis using Excel (e.g., scenario modeling, sensitivities) and/or Python-based modeling workflows. 

• Experience working with complex business and finance datasets (e.g., P&L, GL, cost centers, product hierarchies) and translating them into analysis-ready tables, dashboards, and insights. 

• Strong analytical thinking and financial acumen, including experience with forecasting, variance analysis, KPI design, and translating analysis into business cases and recommendations. 

• Excellent communication and presentation abilities, with a proven ability to explain complex technical concepts to diverse audiences. 

• Ability to manage multiple priorities and deliver results in a fast-paced environment. 

Preferred Qualifications 

• Demonstrated experience designing and deploying executive dashboards for financial and operational performance (e.g., revenue, margin, spend, working capital, and service KPIs). 

• Advanced proficiency with BI and analytics tools (e.g., Power BI, Tableau, Qlik) including dashboard design, data modeling concepts, and metric governance. 

• Familiarity with Mopar systems, after-sales performance metrics, and common finance processes/tools (e.g., ERP/GL concepts, budgeting/forecasting cadence, and close/variance routines). 

• Knowledge of advanced analytics techniques (e.g., time-series forecasting, segmentation, and optimization) is a plus. 

• Exposure to applied AI/ML techniques (e.g., supervised learning, forecasting, NLP) and ability to evaluate model performance and business impact. 

• Relevant certifications (e.g., Power BI, Tableau, CFA, CPA) are a plus.