Curious, Critical Thinker, Empathetic, and Ethically Responsible: Essential Soft Skills for Data Scientists in Software Engineering
Bibliographic record
Abstract
Background. As artificial intelligence and AI-powered systems continue to grow, the role of data scientists has become essential in software development environments. Data scientists face challenges related to managing large volumes of data and addressing the societal impacts of AI algorithms, which require a broad range of soft skills. Goal. This study aims to identify the key soft skills that data scientists need when working on AI-powered projects, with a particular focus on addressing biases that affect society. Method. We conducted a thematic analysis of 87 job postings on LinkedIn and 11 interviews with industry practitioners. The job postings came from companies in 12 countries and covered various experience levels. The interviews featured professionals from diverse backgrounds, including different genders, ethnicities, and sexual orientations, who worked with clients from South America, North America, and Europe. Results. While data scientists share many skills with other software practitioners—such as those related to coordination, engineering, and management—there is a growing emphasis on innovation and social responsibility. These include soft skills like curiosity, critical thinking, empathy, and ethical awareness, which are essential for addressing the ethical and societal implications of AI. Conclusion. Our findings indicate that data scientists working on AI-powered projects require not only technical expertise but also a solid foundation in soft skills that enable them to build AI systems responsibly, with fairness and inclusivity. These insights have important implications for recruitment and training within software companies and for ensuring the long-term success of AI-powered systems and their broader societal impact.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.046 | 0.117 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.031 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".