---
esco_uri: http://data.europa.eu/esco/skill/4d39b27a-b34f-4c89-8d46-df844d42f728
esco_label: "increase the impact of science on policy and society"
relation: essential
onet_soc: 15-2051.00
source: model-knowledge
confidence: high
qa_count: 0
generator: gemma3:27b (prompt-designed and spot-checked by Claude)
generated: 2026-07-10
---

# increase the impact of science on policy and society — Data Scientist

For a Data Scientist, 'increasing the impact of science on policy & society' isn’t about *doing* the policy itself, but ensuring your analyses are usable and understood by those who do. This manifests in tasks like translating complex model outputs (e.g., predictive risk scores, causal inference results) into clear, actionable insights for non-technical stakeholders – think policymakers, program managers, or community groups. It means going beyond simply delivering a report; it's about crafting compelling visualizations, concise summaries highlighting key implications, and proactively anticipating questions around limitations & uncertainty. Tools include storytelling with data (Tableau, Power BI), effective communication frameworks (e.g., 'so what?' tests for every finding), and potentially even building interactive dashboards allowing stakeholders to explore the data themselves.

A common pitfall is ‘solution-seeking’ without understanding the *problem* as defined by those impacted. You might build a fantastic model predicting homelessness, but if it doesn't align with how local housing authorities collect data or their existing priorities, it will be ignored. Another is failing to acknowledge uncertainty – overstating confidence in predictions erodes trust and can lead to poor decisions. Good looks like actively seeking feedback from stakeholders *throughout* the project lifecycle (not just at the end), framing findings in terms of policy-relevant metrics (cost savings, improved outcomes for specific populations), and being transparent about data limitations.

Specifically, this competence often involves building 'explainable AI' models or using techniques like SHAP values to demonstrate *why* a model is making certain predictions. This isn’t just technical rigor; it’s crucial for accountability and trust when decisions impact people’s lives. It also means documenting your work thoroughly – not just code, but the entire analytical process – so others can understand and validate your findings. Think of yourself as a translator between the world of data and the world of action.

Finally, consider that 'impact' isn't always immediate or easily measurable. Sometimes it’s about contributing to a longer-term understanding of a problem, informing future research directions, or building capacity within an organization to use data more effectively. Proactive communication – sharing insights through blog posts, presentations at relevant forums, or even informal conversations – can amplify your influence beyond specific projects.

## Weitere Anreicherung

Stage-2 source for future practitioner grounding: arXiv cs.LG/stat.ML (ML preprints).
