4 Model Jobs in Oberösterreich
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RESPONSIBILITIES
- 4+ years of experience in Data Science. Previous experience working as a Machine Learning Expert is a plus
- Masters or Doctorate in relevant field, for instance: Mathematics, Statistics, Science (including data, behavioural, social, econ, ...)
- Good command of English, both spoken and written. German a plus
- Programming: know at least one of R, Python, SQL with the ability and willingness to learn the other two
- Data Literacy: ability to prepare datasets ensuring top quality such that essence of said data and the implications for the problem at hand can be grasped quickly
- Statistical Reasoning: theoretical and applied understanding of Stats, Probability and ML Algorithms and know how to use these tools to reduce uncertainty in a business context (regression, visualization)
- Technical Literacy: applied understanding of modern computing allows the candidate to do things which go beyond the strict definition of Data Science (git, API calls, web crawling, …)
- Presentation Skills: present coherent data stories at the appropriate level of abstraction given the audience
- Stakeholder Management: skilled at building mutually beneficial connections with functional stakeholders
- Pragmatic Critical Thinking: intuitively consider relevant costs/benefits in all decisions and act accordingly
- Outcome Driven: highly motivated to add value and to demonstrate that impact to the organization
- Scientific Reasoning/ Scoping: ability to define and formulate new questions, in addition to answering given ones
- Grit: proven capability to see things through to the end even if initial feedback is discouraging
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RESPONSIBILITIES
- Support the formulation of technology strategy in response to spotted disruptive external or internal forces and its operationalization.
- Drive IT Landscape Engineering through proactive and deliberate management of the IT landscape beyond investment planning horizons. This involves operationalizing strategy, identifying drivers for change, and defining architecture directions to address these with stakeholders.
- Analyze business strategy and business future-state capabilities to identify Strategic Themes to detect gaps and opportunities for improvement in the current IT Landscape in line with the IT Strategy to enable the business achieving targeted outcomes.
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RESPONSIBILITIES
- Experience in user research and talking to stakeholders to bridge the gap between the problem of a user and actual AI implementation options
- Proven experience in developing AI-powered solutions, from research to production-ready implementation.
- Strong foundation in modern AI/ML frameworks, patterns and tooling, especially in Generative AI, covering concepts like prompt engineering, RAG, LLM as a judge, fine-tuning, and so on.
- Familiarity with cloud platforms (preferrable AWS) and LLMOps & MLOps pipelines for deploying and scaling AI solutions.
- Expertise in model optimization and evaluation techniques to build robust AI workflows.
- Solid skills in Python (or other relevant languages) and practical experience in applying modern software engineering patterns (like version control systems, merge requests, pair-/mob-programming, testing)
- Strong interest and basic knowledge about Agentic AI and agentic architectures
- Strong communication skills, able to explain technical solutions to non-technical audiences.
- Excellent problem-solving and analytical skills, with a proactive approach to challenges.
- Ability to work collaboratively with cross-functional teams.
- Ability to move fast in an environment where things are sometimes loosely defined and may have competing priorities or deadlines
- Enjoy owning the problems end-to-end, and are willing to pick up whatever knowledge you're missing to get the job done
- Affinity for media and broadcast domain is a plus
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