💼 Role Overview
We are looking for an AI Value & Business Insights Lead & Data Architect who combines strong business expertise with hands-on data architecture capability, to transform AI outputs and analytical results into clear, actionable business insights for Mobility Aftermarket . You will work closely with Bosch AI/analytics teams and MA business stakeholders to ensure AI solutions lead to better decisions, measurable impact, and continuous improvement. This role focuses on interpreting AI results to bring business value and insight.
This role owns the full value chain - Deep understanding of Mobility aftermarket (or ailke) business and interpret the AI value by building Data solutions
- Understanding business challenges (Mobility aftermarket or alike)
- Designing and managing data pipelines & cloud structures
- Enabling AI/analytics solutions
- Translating outputs into measurable commercial impact
- You will not only interpret AI results — you will ensure the right data foundation exists to generate reliable, scalable insights.
🎯 What you will do (Key Responsibilities)
1. Business-Driven AI & Data Strategy
- Understand key MA business processes (sales, pricing, trade marketing, supply chain, customer behavior).
- Identify high-value AI and analytics use cases.
- Translate commercial questions into structured data and analytical requirements.
- Ensure AI initiatives directly support revenue growth, margin improvement, and efficiency.
2. Data Architecture & Pipeline Ownership
- Design and manage scalable data pipelines for MA use cases.
- Structure, clean, and integrate data from multiple sources (ERP, Redmesh, eCommerce, etc.).
- Manage and optimize cloud-based data environments.
- Ensure data quality, governance, and reliability for AI/analytics solutions.
- Collaborate with IT and central data teams to align architecture standards.
3. AI Enablement & Insight Generation
- Work with AI/ML teams to develop and refine use cases.
- Interpret model outputs and validate business relevance.
- Identify patterns such as: Price inquiries vs order conversion gaps. Channel-specific margin erosion, Customer churn signals etc.
- Turn analytical findings into clear business recommendations
4. Value Realization & KPI Management
- Define measurable KPIs for AI and data initiatives.
- Track business impact (revenue uplift, margin improvement, cost reduction).
- Ensure continuous feedback loops to improve models and data structures.
- Present insights and progress to MA leadership.