АНАЛІЗ ВИМОГ ДО ПОЗИЦІЙ DATA ANALYST ТА DATA SCIENTIST НА РИНКУ ПРАЦІ
Bibliographic record
Abstract
The labour market in the field of data analytics was analysed in this article. Specifically, there were analysed the requirements for vacancies Data Analyst and Data Scientist. The analysis covered such job search platforms as Work.ua (Ukraine), Robota.ua (Ukraine), DOU (Ukraine), Djinni (Ukraine), The Muse (USA), Technojobs (France), Careerjet (Canada), Profesia (Czech Republic, Slovakia), Reed (UK). The study was consisted of 4 stages: semi-automated data collection, processing of key job requirements, data storage and structuring, and data analysis. Such tools as Python (streamlit, pandas, re) and AirTable were used in the study. The structure and style of job postings on job search sites were analysed. Patterns were discovered that could be used to derive key indicators. Algorithms for replacing missing data, such as work experience, were discovered. An application was developed to automate the process of analysing of the text of vacancies. The user interface was implemented using the Streamlit framework. A total of 1000 vacancies were collected for analysis. Of these, 586 were collected from Ukrainian sources, and the rest were from abroad. The research was based on the analysis of the following indicators: the frequency of vacancies by position (junior, middle, senior); the type of companies looking for Data Analyst or Data Scientist; the mode of work (offline, online and hybrid); the level of English proficiency; visualisation tools that the candidate should know; preferred programming language; desired database; requirements for office tools; requirements for knowledge of specialised libraries for data analysis and machine learning. All these indicators were quantitatively analysed and a conclusion was drawn. A table, that clearly demonstrates the researched trends, have been developed by authors.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.037 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.009 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".