Status: July 8, 2026. The first job after training or university often feels contradictory: companies are looking for talent, but job advertisements already demand practical experience. This is particularly visible in data and AI-related jobs. A current 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 just typing "junior" into a job board. Career starters must search more broadly, prove their skills more concretely, and also check 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 can strategically search for entry-level jobs, which signals in job advertisements are important, and how career starters can become visible despite sparse junior listings.
Why starting a career seems 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. Within the identified data and AI roles, only a small portion was explicitly marked as a junior position. At the same time, roles such as Data Engineer, Data Scientist, Data Analyst, Business Intelligence, or AI Engineer dominated.
For beginners, this means: The lack of opportunities is not necessarily due to their own qualifications, but often to the language of the advertisements. Some companies advertise "Associate," "Trainee," "Analyst," "Coordinator," "Support," "Operations," or simply a specialist role without a seniority level. Anyone who only searches for "junior" overlooks such opportunities.
The AMS Career Compass also shows that entry paths can look very different: direct jobs, trainee programs, internships, temporary entry-level positions, or initial specialist roles with a learning component. The decisive factor is whether tasks, expectations, and onboarding realistically fit together.
Broaden search terms
Many career starters search too narrowly. Anyone who only enters "junior marketing," "junior developer," or "career starter Vienna" gets only a slice of the market. A search matrix consisting of role, competence, and entry form is better. For data and AI-related jobs, these can be, for example: Data Analyst, Reporting, BI, CRM, Analytics, Process Analysis, AI Project Assistance, 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.
With Job search online and mobile as well as "all jobs," the AMS offers 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 provide suitable interviews, and which only result in scatter loss?
Check job advertisements for learning curves
Not every advertisement without a junior title is unsuitable. Conversely, not every junior advertisement is a good entry point. Decisive factors are indications of onboarding, team structure, mentoring, concrete tools, scope of responsibility, and mandatory requirements. If an advertisement demands ten tools and five years of experience, it is probably not realistic. But if "initial experience," "interest in," "training," "teamwork," or "development opportunities" appear, an application can make sense.
For current job advertisements, a close look is worth it. The article Summer on the job market: What applicants can learn from the AMS figures now shows why search routine and industry movement are important right now. For career starters, it also counts: do not just wait for the perfect advertisement, but recognize suitable learning environments.
A simple rule of thumb helps: If about 60 to 70 percent of the requirements are provable and the remaining points seem learnable, an application can be worthwhile. If core tasks are unclear or the role factually demands senior responsibility without guidance, one should be cautious.
Make skills provable
Beginners often have fewer years of work, but not automatically little experience. Projects, theses, internships, holiday jobs, open-source contributions, volunteer organization, side jobs, competitions, or self-created analyses can be relevant evidence. They just need to be described in such a way that employers recognize the benefit.
Especially with data and AI topics, "basic knowledge of Python" is rarely enough. Better is: "Created dashboard for evaluating sales data," "cleaned and visualized survey with 450 responses," "tested AI tool for text drafts and documented quality rules," or "automated Excel reporting." Such examples show the way of working, not just tool names.
The AMS Career Compass can help to structure professions, activities, and requirements. Anyone preparing their own application should also check their documents carefully: Checking application documents: What matters before sending.
Present AI competence without exaggeration
AI competence is an advantage if it is described concretely and responsibly. It is not about pretending to be an AI expert if you use ChatGPT for text ideas. It is about showing how you use digital tools reflectively: preparing research, structuring data, accelerating routine tasks, checking results, and observing data protection and sources.
Jobspot has already addressed this in the article Working, learning, applying: Why digital skills are becoming more important in the labor market. For entry-level jobs, the practical translation is decisive: Which task was completed better, faster, or more comprehensibly?
AI can also support the application process itself, but it cannot replace your own argumentation. Anyone using AI for cover letters or resumes should check the wording, add personal examples, and not adopt invented experiences. This fits with Applying with AI: Where ChatGPT helps and where humans need to refine.
Strategically build the first professional experience
If direct junior jobs are scarce, entry can succeed via adjacent roles. 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 job with digitalization projects can later lead to a specialist role.
It is important not to choose these bridge roles arbitrarily. Applicants should check whether they are building relevant skills there: data understanding, customer contact, project work, tools, industry knowledge, documentation, communication, or process knowledge. Anyone who can prove what they have learned and improved after six months has stronger arguments in the next application process.
Similar principles apply to applicants who do not come directly from training or university. The article Making a lateral career move: How experience fits a new role shows how existing experience can be translated into new target roles.
Job interview: Show learning ability concretely
In the interview, career starters should not only emphasize motivation. A clear learning strategy is better. Good answers combine interest, example, and next steps: "I worked with SQL basics during my studies, cleaned data in my final project, and would like to deepen reporting and data quality in the first half of the year." That sounds more resilient than "I learn quickly."
AMS application tips recommend preparing your own documents and the company well. For beginners, this also means: have three project examples ready, explain your own role in them, and honestly say 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 worth it: What requirements actually came up? Which examples were convincing? Which gap became visible? This turns every application into market research, not just an acceptance or rejection.
Checklist: Finding entry-level jobs
- Combine search terms from role, tool, industry, and entry form.
- Do not just search for "junior," but also for associate, trainee, analyst, assistant, or coordinator.
- Check job advertisements for onboarding, team structure, and realistic mandatory requirements.
- Formulate projects, internships, holiday jobs, and theses as concrete work examples.
- Present digital and AI skills with responsible practical evidence.
- Use bridge roles if they build relevant experience for the target role.
- Test several search agents and sharpen them after two weeks based on real hits.
- Explain learning ability in the interview with an example, goal, and next step.
- Evaluate rejections: is specialist knowledge, practice, industry, salary range, or fit missing?
- If in doubt, use AMS counseling, career centers, mentors, or industry contacts.
Conclusion: The first job does not have to be called perfect
A good entry-level job does not always have "junior" in the headline. Often it is a role in which tasks, team, and learning curve fit together. Anyone who only searches for the ideal title overlooks possible bridges. Anyone who makes skills provable and searches more broadly significantly increases their chances.
For career starters 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 fields, and use every piece of feedback. This way, the first job does not become a coincidence, but a plannable stage.
