As of: 8 July 2026. The first job after vocational training or university often feels contradictory: companies look for talent, but job ads already demand practical experience. This is especially visible in data- and AI-related jobs. A recent Austrian labor market report on data and AI skills shows many specialist profiles but comparatively few clearly advertised junior roles.
Finding entry-level jobs therefore means more than typing "Junior" into a job board. Career starters need to search more broadly, demonstrate skills more concretely, and also consider roles that do not obviously sound like entry-level positions. This applies not only to IT, but also to marketing, controlling, HR, administration, industry, consulting and the public sector.
This article shows how applicants in Austria strategically search for entry-level jobs, which signals in job ads are important, and how career starters can become visible despite sparse junior listings.
Why starting a career feels more selective
The labor market is not closed, but it has become more demanding. The Data and AI labor market report by Data:Unplugged, Brutkasten and other partners analyzed around 24,900 job advertisements from Austria. Among the identified data and AI roles, only a small portion were explicitly labeled as junior positions. At the same time, roles such as Data Engineer, Data Scientist, Data Analyst, Business Intelligence or AI Engineer dominated.
For newcomers this means: the shortage is not necessarily due to their own qualifications, but often to the language of the ads. Some companies post "Associate", "Trainee", "Analyst", "Coordinator", "Support", "Operations" or simply a specialist role without a seniority level. Those who only search for "Junior" miss such opportunities.
The AMS-Karrierekompass also shows that entry paths can look very different: direct jobs, trainee programs, internships, fixed-term entry-level positions, or initial specialist roles with a learning component. The decisive factor is whether tasks, expectations, and onboarding realistically match.
Broaden your search terms
Many career starters search too narrowly. Anyone who only enters "Junior Marketing", "Junior Developer", or "Career starter Vienna" only sees a fraction of the market. It is better to use a search matrix consisting of role, competence, and entry type. For data and AI-related jobs, these could be, for example: Data Analyst, Reporting, BI, CRM, Analytics, Process Analysis, AI Project Assistant, Automation, Research, Operations, or Product Data.
In addition, there are search terms such as Trainee, Graduate, Associate, Entry Level, Assistant, Coordinator, working student, internship with a prospect of being hired, or Junior. In Austria, German and English job titles are common in parallel. Those who use both will find more.
The AMS offers with Job search online and mobile as well as "alle jobs" a broad search across many sources. It is important not only to set up one search agent, but to test several variants and evaluate them after two weeks: Which terms deliver suitable interviews, which only result in scatter loss?
Check job advertisements for learning curve
Not every job ad without the 'Junior' title is unsuitable. Conversely, not every junior ad is a good entry point. What matters are indications of onboarding, team structure, mentoring, specific tools, scope of responsibilities and must-have requirements. If an ad demands ten tools and five years of experience, it's probably not realistic. But if it includes 'first experience', 'interest in', 'training', 'teamwork' or 'development opportunities', applying can make sense.
It's worth taking a close look at current job listings. The article Summer in the job market: What applicants can learn now from the AMS figuresIt explains why a regular search routine and changes in the industry are particularly important right now. For entry-level job seekers there's an additional point: don't just wait for the perfect ad, but recognize suitable learning environments.
A simple rule of thumb helps: if about 60 to 70 percent of the requirements can be documented and the remaining points appear learnable, submitting an application can be worthwhile. If core tasks are unclear or the role effectively demands senior-level responsibility without guidance, one should be cautious.
Make skills demonstrable
Entry-level candidates often have fewer years on the job, but not necessarily little experience. Projects, theses, internships, summer jobs, open-source contributions, volunteer organizing, side jobs, competitions, or self-created analyses can be relevant evidence. They just need to be described in a way that lets employers see the value.
Especially for data and AI topics, 'basic knowledge of Python' is rarely enough. Better examples are: 'created a dashboard to analyze sales data', 'cleaned and visualized a survey with 450 responses', 'tested an AI tool for draft texts and documented quality rules', or 'automated Excel reporting'. Such examples show how you work, not just tool names.
The AMS-Karrierekompass can help structure occupations, tasks and requirements. Anyone preparing their own application should also carefully check their documents: Checking application documents: What matters before sending.
Present AI skills without exaggeration
AI skills are an advantage when they are described concretely and responsibly. It's not about presenting yourself as an AI expert when you use ChatGPT for text ideas. It's about showing how you use digital tools thoughtfully: preparing research, structuring data, speeding up routine tasks, checking results, and considering data protection and sources.
jobspot has already covered this in the article Work, learn, apply: Why digital skills are becoming more important in the job market addressed. For entry-level jobs, the practical translation is decisive: Which task was completed better, faster, or more transparently?
AI can also assist during the application itself, but it should not replace your own argumentation. If you use AI for cover letters or your CV, you should review the phrasing, add personal examples, and not include fabricated experiences. This fits with Applying with AI: Where ChatGPT helps and where humans need to refine.
Strategically build your first professional experience
If direct junior jobs are scarce, entry via adjacent roles can work. A marketing assistant with analytics tasks, an entry-level controlling position with reporting, an HR role with recruiting data, a support job with process documentation, or an administrative role with digitalization projects can later lead to a specialist role.
It's important not to choose these bridge roles arbitrarily. Applicants should check whether they can build relevant skills there: data literacy, customer contact, project work, tools, industry knowledge, documentation, communication, or process knowledge. Anyone who can demonstrate after six months what they have learned and improved will have stronger arguments in their next application process.
For applicants who are not coming directly from an apprenticeship or university, similar principles apply. The article Making a career change: How experience fits the new role shows how existing experience can be translated into new target roles.
Job interview: Demonstrating learning ability concretely
In the interview, career starters should not just emphasize motivation. A clear learning strategy is better. Good answers combine interest, an example, and next steps: "I worked with SQL basics during my studies, cleaned data in my final project, and would like to focus on reporting and data quality in the first half of the year." That sounds more reliable than "I learn quickly."
AMS application tips recommend preparing your own documents and the company well. For beginners, this additionally means: have three project examples ready, explain your own role in them, and honestly state where there is still a need for learning. Employers do not hire beginners because they can do everything, but because they see potential, structure, and reliability.
After the interview, a short follow-up is worthwhile: What requirements actually came up? Which examples were convincing? Which gap became visible? This way, every application becomes market research, not just an acceptance or rejection.
Checklist: Finding entry-level jobs
- Combine search terms from role, tool, industry, and entry type.
- Don't just search for "Junior", also look for Associate, Trainee, Analyst, Assistant, or Coordinator.
- Check job postings for onboarding, team structure, and realistic must-have requirements.
- Frame projects, internships, summer jobs, and theses as concrete examples of work.
- Demonstrate digital and AI skills with examples of responsible practical application.
- Use bridging roles when they help build relevant experience for the target role.
- Test multiple search agents and fine-tune them after two weeks based on real matches.
- In interviews, explain your ability to learn with an example, a goal, and the next step.
- Evaluate rejections: is it missing expertise, practical experience, industry alignment, salary range, or overall fit?
- If unsure, use AMS counseling, the career center, female mentors, or industry contacts.
Conclusion: The first job doesn't have to be perfect.
A good entry-level job doesn't always have "Junior" in the title. Often it's a role where tasks, team and the learning curve align. Those who only look for the ideal title overlook possible bridges. Those who make their skills demonstrable and search more broadly significantly increase their chances.
For entry-level job seekers in Austria, the most important step is therefore not the perfect self-description, but a systematic search process: find realistic roles, show concrete examples, openly name learning areas and use every piece of feedback. That way the first job doesn't become a matter of chance, but a planned milestone.
