---
name: data-scientist
description: "Occupational skill for the role 'data scientist' (also: data expert, research data scientist, data research scientist, electromobility data scientist). Use when the user asks for typical data scientist work such as: Analyze, manipulate, or process large sets of data using statistical software.; Apply feature selection algorithms to models predicting outcomes of interest, such as sales, attrition, and healthcare use.; Apply sampling techniques to determine groups to be surveyed or use complete enumeration methods."
---

# Data Scientist

Data scientists find and interpret rich data sources, manage large amounts of data, merge data sources, ensure consistency of data-sets, and create visualisations to aid in understanding data. They build mathematical models using data, present and communicate data insights and findings to specialists and scientists in their team and if required, to a non-expert audience, and recommend ways to apply the data.

## Core workflow

1. Analyze, manipulate, or process large sets of data using statistical software.
2. Apply feature selection algorithms to models predicting outcomes of interest, such as sales, attrition, and healthcare use.
3. Apply sampling techniques to determine groups to be surveyed or use complete enumeration methods.
4. Clean and manipulate raw data using statistical software.
5. Compare models using statistical performance metrics, such as loss functions or proportion of explained variance.
6. Create graphs, charts, or other visualizations to convey the results of data analysis using specialized software.
7. Deliver oral or written presentations of the results of mathematical modeling and data analysis to management or other end users.
8. Design surveys, opinion polls, or other instruments to collect data.

## How to use this skill

- Read `references/profile.md` for the occupation profile and scope.
- Consult `references/tasks.md` for the full task and activity inventory.
- Check `references/skills.md` for essential vs. optional competences.
- Check `references/tools.md` for the software commonly used in this role.

## Key competences (essential)

- apply for research funding
- apply research ethics and scientific integrity principles in research activities
- build recommender systems
- collect ICT data
- communicate with a non-scientific audience
- conduct research across disciplines
- data engineering
- data ethics
- data mining
- data models
- data science
- data visualisation software
- deliver visual presentation of data
- demonstrate disciplinary expertise
- design database scheme

## Hot technologies

- IBM SPSS Statistics
- SAS
- TensorFlow
- The MathWorks MATLAB
- Docker
- GitHub
- Kubernetes
- Alteryx software
- Apache Spark
- Google Looker Analytics

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*Sources: ESCO v1.2.1 (http://data.europa.eu/esco/occupation/258e46f9-0075-4a2e-adae-1ff0477e0f30), O*NET 30.3 (15-2051.00). See manifest.json for licensing/attribution.*
