## 5 Results
### Studies on Employers  
**Employer profile. ** This study's survey gathered responses from 38 companies to explore their needs and expectations regarding the training of specialists in applied AI. Most respondents (86.84%) represented private organizations, with only 13.16% indicating their affiliation with public organizations. Regarding the size of their organizations, over half of the respondents (52.63%) described their businesses as small, over 26.32% described them as medium-sized, and 21.05% described them as large--that is, as employing more than 250 people. The majority of respondents--36.84%--were employed in the IT services \begin{table}
\begin{tabular}{l c} \hline \hline
**Task** & **Percentage** \\ \hline Classification & 68.92\% \\ Regression & 58.11\% \\ Clusterization & 35.14\% \\ Image classification & 28.38\% \\ Image captioning & 22.97\% \\ Natural language processing & 22.97\% \\ Image segmentation & 22.97\% \\ Speech recognition & 12.16\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Key ML tasks in demand on the job market. industry. Miscellaneous categories (15.79%) and other product startups (15.79%) comprised further noteworthy segments. Additionally featured were IT outstaffing, hybrid software development, computer technology sales, offshore programming, game technologies, and IT outsourcing. A few businesses were product startups that worked in fields including MES and administrative tasks, software development and IT outsourcing, IT services, IT financing, IT services, and research and education. **Employers' activity areas. ** The primary fields of activities declared by the organizations varied, with manufacturing and development (47.37%), design (39.47%), consulting (44.74%), customer service (42.11%), and research (39.47%) being the main domains. These businesses provide applications and solutions for a wide range of industries, including public services, education, finance, sales, marketing, healthcare, and transportation. Regarding the usage of AI in business activities, most of the organizations (86.64%) indicated that they currently utilized AI, while a small portion (13.16%) stated that they had intentions to do so in the future. **Job positions offered by employers. ** Regarding AI-related job positions, data engineer had the highest percentage of job offerings (58.33%), followed by data analyst (55.56%) and data scientist (44.44%). Other positions such as technical recruiter, security engineer, and database manager had lower percentages of job offerings. Regarding the necessary experience, 41.67% of job openings did not specify any prior AI experience. Most jobs required experience, which ranged from brief training for up to a year (25%) to one to three years (27.78%). Only 5.56% of career positions demanded more than five years of experience. **AI competencies needed by employers. ** The survey also examined the general competencies needed for AI-related roles. The highest proportion (60%) was ascribed to the ability to identify issues with data integrity, privacy, and algorithmic and prejudice bias. Identifying relevant performance criteria for assessing machine learning algorithms, recognizing the value of machine learning techniques, and summarizing the main fields of artificial intelligence and its applications were among the other highly rated talents. Lower percentages were found for competencies, including arguing for the implications of judgments made based on machine learning results and expressing information in probabilistic and logical formalisms. Regarding the character traits needed for AI and data science workers, the participants emphasized the significance of acknowledging the limitations and history of AI, being flexible in algorithm development, using machine learning in an ethical and responsible manner, and presenting findings in a thorough and morally sound manner.