Connected Services Analytics & AI Enablement Lead
Listed on 2026-08-31
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IT/Tech
AI Engineer (Applied/Software), Data Analyst, Data Science Manager, Data Engineering
Connected Services Analytics & AI Enablement Lead
As part of the Data & Diagnostic insights initiative, we are looking for a Connected Services Analytics & AI Enablement Lead to accelerate the adoption and value creation from connected vehicle data through advanced analytics, AI, and self-service capabilities.
In this role, you will be responsible for transforming connected services & digital product data into actionable insights, enabling business teams and engineering stakeholders to make data-driven decisions. You will lead the development of analytics solutions, define reusable data assets, and promote AI-powered approaches to improve customer understanding, product performance, and operational efficiency.
Your key responsibilities will include:
Analytics & Data Product Enablement
- Translate business needs and Connected Services challenges into scalable analytics solutions and data requirements.
- Define and develop analytics use cases leveraging connected vehicle data, feature usage data, customer behavior data, and operational data.
- Design reusable analytics assets and data products to accelerate insight generation and decision-making.
- Ensure data consistency, quality, representativeness, and reliability through appropriate validation processes.
- Develop advanced analytics methodologies to uncover customer behaviors, product usage patterns, and feature performance insights.
- Build and maintain dashboards and visualization solutions to facilitate insight sharing and self-service analytics adoption.
AI & Intelligent Solution Enablement
- Identify and develop opportunities to leverage Artificial Intelligence and Generative AI to enhance analytics capabilities.
- Contribute to the design and deployment of AI-powered solutions such as AI assistants, analytics agents, and knowledge retrieval systems.
- Define business rules, analytical logic, and data foundations required to enable AI-driven applications.
- Prepare, structure, and curate datasets to support AI model development and intelligent workflows.
- Collaborate with Data Scientists and AI experts to industrialize machine learning and GenAI use cases.
Platform & Technical Leadership
- Design and implement scalable data processing workflows using Python, SQL, Spark, and cloud-based technologies.
- Leverage platforms such as Databricks to develop analytics pipelines and data processing solutions.
- Ensure best practices in data governance, documentation, monitoring, and lifecycle management.
- Promote standardization and reuse of analytics solutions across Connected Services domains.
- Support the adoption of self-service analytics tools by providing frameworks, methodologies, and guidance.
Basic Qualifications:
- Bachelor's degree in Computer Science, Data Science, Artificial Intelligence, Engineering, or a related field.
- Minimum of 8 years of experience in Data Analytics, Data Science, AI enablement, or a similar role.
- Ability to translate business challenges into scalable, reusable, data-driven analytics solutions delivering business outcomes.
- Good understanding of automotive domains, especially Connected Services, vehicle features, customer behavior analytics, or mobility services.
- Experience working with large-scale data platforms and distributed processing environments.
- Strong proficiency in:
Python, SQL, Spark / PySpark, Data visualization tools (Power BI or equivalent) - Hands-on experience with Databricks or similar cloud data platforms.
- Experience with AI and Generative AI concepts:
- AI agents and intelligent workflows
- Large Language Models (LLMs)
- Retrieval-Augmented Generation (RAG)
- Machine Learning lifecycle concepts
- Strong communication and collaboration skills with technical and non-technical stakeholders.
Preferred Qualifications:
- Master's degree in Computer Science, Data Science, Artificial Intelligence, Engineering, or a related field.
- Knowledge of data governance, and data quality management best practices.
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