Objective of job

Architectural design and full-lifecycle iteration of Artificial Intelligent features within the B2B retail ecosystem, coordinate and close collaboration with internal stakeholders in building digital infrastructure for MB dealers:
- AI Scenario Blueprint: A comprehensive map of the dealer journey (Sales, After-sales, Inventory) identifying high-value AI intervention points (e.g., intelligent customer conversation & sales process analysis).
- Algorithm Logic & Functional Specifications: Technical BRDs that define model inputs/outputs, prompt orchestration strategies, and RAG (Retrieval-Augmented Generation) architectures for dealer-facing assistants.
- AI Evaluation & Benchmarking Framework: A structured tool to measure model performance (accuracy, latency, hallucination rates) against business KPIs (conversion rates, labor-hour reduction).
- PMO Lifecycle Dashboard: Integrated progress tracking including development velocity, data-labeling milestones, and model deployment health.
- Identify business process "breakpoints" (inefficiencies) and deploy AI-native solutions that deliver measurable ROI for dealers and the enterprise.
- Risk & Compliance Matrix: Documentation of data privacy safeguards, model safety protocols, and regional regulatory compliance for AI applications.

Main tasks

1. Scenario Discovery & Demand Mining
- Deep-diving into dealer workflows to identify "breakpoints"; conducting "AI-first" business process redesign; defining the product-market fit for internal AI tools, coordinating and close collaboration with internal stakeholders in building digital infrastructure for MB dealers
2. Product Design & Technical Logic
- Writing high-fidelity BRDs; collaborating with algorithm engineers on model selection and fine-tuning strategies; designing frontend AI interaction patterns (UI/UX for AI)
3. Evaluation & Data Iteration 
- Establishing the "Core Indicator System" for AI features; analyzing "bad cases" to refine model performance; managing the data-loop from user feedback to model improvement
4. PMO & Risk Management
- Leading the end-to-end delivery cycle; managing development toolkits; ensuring cross-system integration stability and AI safety compliance.
5. Ecosystem & Industry Research
- Tracking industry trends in Generative AI and B2B retail tech to ensure our systems remain at the forefront of the "Intelligent Upgrade" curve.

Becoming part of Mercedes-Benz means finding your individual role and workspace to unleash your talents to the fullest. It means becoming your best self in a global automotive company that has the goal to build the most desirable cars in the world. Empowered by visionary colleagues who share the same pioneering spirit.

Together for excellence.

Education
Bachelor’s or Master’s degree in Computer Science, Data Science, Information Systems or Business administration.

Skills and Knowledge
- 5+ years in Product Management, with a significant portfolio in B2B SaaS or Enterprise Internal Systems, 2+ years in AI product management. 
- Scenario Discovery: Exceptional ability to "dock" with cross-functional departments (Finance, Logistics, Sales) to uncover hidden pain points.
- Stakeholder Navigation: Proven track record of aligning technical algorithm teams with non-technical business leaders.
- Resilience & Agility: Comfortable navigating the ambiguity of emerging technologies and pivoting based on rapid prototyping results.

Functionality Skills
- Hands-on experience in deploying Generative AI (LLMs), predictive analytics, or recommendation engines in a production environment.- Deep understanding of the AI stack: Fine-tuning, Prompt Engineering, Vector Databases, and Agentic workflows.
- Ability to perform Abstraction & Modeling of complex business logic into standardized system requirements.
- Proficiency in data analysis tools to drive evidence-based product iterations.