Aftersales Business Analytics and Insights Analyst

Stellantis•Auburn Hills, MI

18h

About The Position

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.

Requirements

Bachelor’s or Master’s degree in Data Science, Computer Science, Industrial Engineering, Finance, Economics, Accounting, Statistics, or a related quantitative field.
Minimum 8 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.

Nice To Haves

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.

Responsibilities

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.
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.
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.