At Statista, we’re all about facts and data, for we are the world’s leading business data platform. By providing reliable and easy-to-use data as well as various data analytics products and services, we empower people worldwide to make fact-based decisions.
Founded in Hamburg in 2007, we have quickly grown into a global company with offices in major cities such as London, New York, Berlin and Tokyo. And we still have a lot of plans. Our constant growth does not only prove our success, but also keeps creating new development and career opportunities for our employees.
We value and celebrate our diverse culture. You are welcome here for who you are, no matter where you come from, what you look like, or whether you prefer bar graphs to pie charts. Your story matters – keep writing it as part of our team.
Are you ready to join us?

YOUR SKILLS

• At least 2-3 years experience in leading a product-oriented Engineering team in the AI engineering sphere
• AI Engineering knowledge and experience, incl. also experience in evaluation techniques and quality assurance in AI contexts.
• Experience working with with LLMs and RAG: Prior knowledge and experience in retrieval augmented generation (RAG) and LLM based chats is required.
• Experience with LangChain, LangGraph, and LangSmith or Langfuse is favorable.
• Hands-on: Strong Python background and experience with tooling related to AI Engineering, experience in shaping scalable, well-maintainable architectures and infrastructure as code
• Solid track record in developing large-scale, user-facing AI services and products.
• AWS: Basic understanding on how to build a scalable, serverless and highly available applications on AWS. Concrete technologies: Lambda, SQS, S3, DynamoDB, RDS, CDK, Datadog, Github Actions.
• Good communication skills, ability to communicate to various stakeholders and across various levels
• Familiar with OKRs and Kanban or Scrum

• Shape vision and roadmap of the team together with the team and UX and Product Manager counterparts based on user and business demands
• Lead the team and help the engineers to grow (regular 1:1 meetings, feedback, daylies, etc.)
• Decide the architecture together with the team and help with implementation, critically review design decisions
• Take over responsibility for the quality of the AI services / products the team is developing as well as for security, stability and imposed costs.
• Further develop the team by hiring new talents if necessary
• Keep up-to-date with latest developments and trends in AI Engineering