Sportradar
AI

Senior Data Scientist [m f d]

Sportradar · Wien, W, AT

Actively hiring Posted 21 days ago

Role overview

Required

What you'll work on

We are looking for a highly skilled Senior Data Scientist (m/f/d) with excellent communication skills who will work alongside a team of talented analysts and engineers, using modern technologies and methods to further develop and expand our Audience capabilities and reporting suite.

You will support stakeholders as a strategic compass - helping them navigate audience growth, segmentation, activation, and performance through actionable insights. You will join a growing team shaping Sportradar’s digital advertising platform, with a strong focus on Audience strategy and expansion.

Operating within a selforganized and agile analytics team, you will play an active role in building and scaling data products that power clientfacing advertising services, working with cuttingedge technologies and highly motivated colleagues.

  • Interpret audience and campaign data to guide strategic decisions and deliver clear, compelling insights for advertisers and stakeholders.
  • Design, build, and maintain scalable audience data models supporting segmentation, activation, and measurement use cases.
  • Participate in the development of datadriven, intelligent, and scalable systems with a focus on quality, observability, and security.
  • Evaluate and monitor existing data sources and audiencerelated products, ensuring data integrity and reliability through regular QA checks.
  • Collaborate with sales, tech, product, and ML teams to design, implement, and refine advanced audience dashboards and activation frameworks.
  • Drive evaluation, evolution, and delivery of audiencefocused solutions, supporting ML and AI initiatives that create measurable value for clients.

What we're looking for

  • Experience with datavisualisation tools and building stakeholderfacing dashboards.
  • Experience in AdTech and audienceactivation ecosystems.
  • Experience with ML technologies and production ML pipelines.
  • Knowledge of testing strategies such as TDD.
  • Experience with identity graphs, segmentation frameworks, or CDPs.
  • Experience creating and maintaining CI/CD workflows.

Interview process

Initial Conversation

A discussion with our Talent Acquisition Partner to learn more about your background, experience, and expectations.

Technical Interview

A conversation with members of the Data team focused on realworld problem solving, production considerations, and your past project work.

TakeHome Case Study

A practical business case reflecting typical challenges in this role, including analysis of a dataset and a short summary of your findings.

Case Presentation & Collaboration Session

You will present your solution, engage in a deeper discussion with the team, and participate in a collaborative paircoding exercise.

Final Conversation

A final discussion centred on team fit, collaboration style, and any remaining questions. An onsite visit may be included where relevant.

Tags & focus areas

Used for matching and alerts on DevFound
Fulltime Remote Data Science Ai