Data Scientist
Også kendt som: Applied Scientist · Advanced Analytics Specialist
Develops statistical and learning models on business data, tests their reliability and translates outcomes into usable conclusions.
The data scientist frames a research question, chooses a method and substantiates how certain an outcome is before decisions rest on it. Unlike the machine learning engineer, the task largely ends with a demonstrably working model rather than keeping it running in production. Selection targets algorithms and programming language, while the real bottleneck is explainability: a model the client neither understands nor can check is either not used or used without anyone knowing the assumptions.
Kompetencer, der skal måles
Dette er adfærds- og kompetencekonstruktionerne i denne profil. Hver enkelt viser, hvilket spørgeskema i hrmforce der måler den.
| Kompetencer, der skal måles | Målniveau | hrmforce instrument | Evalueringsmetode | Adfærdsmæssigt anker på målniveau |
|---|---|---|---|---|
| Numerical reasoning Knockout | N4 | Ability Scan | Cognitive ability test | Combines two data sources, calculates the development over time and explains which calculation step leads to which result. |
| Curiosity and exploring Kompetence (50-rammeværk): Learning Ability | N3 | Big Fifty, 15PF, Motivation | Personality questionnaire | Gathers information on new methods outside the own team and brings findings into the work meeting unprompted. |
| Dealing with ambiguity Kompetence (50-rammeværk): Flexibility | N3 | Big Fifty, Mental Resilience (4C), Competency Check | Situational Judgement Test | Formulates explicit assumptions when information is missing, proceeds on that basis and revises the approach once new data arrives. |
Kort profil
De ti vigtigste kompetencer i denne profil. Den fulde profil med alle kompetencer, adfærdsmæssige ankerpunkter og vurderingsmetoder findes i downloadfilen.
| T | Kompetence | Målniveau | Vægt | Knockout | Evalueringsmetode |
|---|---|---|---|---|---|
| K | Basic statistical interpretation | N5 | 5 | ja | Knowledge test |
| A | Numerical reasoning | N4 | 5 | ja | Cognitive ability test |
| V | Data analysis | N4 | 5 | ja | Work sample test |
| V | Testing hypotheses | N4 | 5 | nej | Work sample test |
| V | Programming | N4 | 5 | ja | Work sample test |
| K | Machine learning fundamentals | N4 | 5 | ja | Knowledge test |
| V | Critical thinking and source evaluation | N4 | 4 | nej | Work sample test |
| V | Visual communication and data storytelling | N4 | 4 | nej | Work sample test |
| V | Instructing and explaining | N4 | 4 | nej | Work sample test |
| V | Data visualisation | N4 | 4 | nej | Work sample test |
Se alle 27 kompetencer i den fulde profil
Vurderingsværktøjer til denne stilling
Work sample test
Representative work sample under standardized conditions
Knowledge test (client-specific)
Professional knowledge test, customized for each client
Competency Check
Behavioral-Level Competency Assessment
Portfolio
Evaluation of prior work against fixed criteria
Structured interview
Behavioral interview with a fixed structure
Ability Scan
Aptitude Test: Verbal, Numerical, and Abstract Reasoning Skills
360 Feedback
Multiple assessments based on behavioral anchors
Big Fifty
Personality questionnaire, 150 items, 25 factors
15PF
Personality Profile, a shorter alternative to the Big Fifty
Motivation
Drivers and sources of motivation
Mental Resilience (4C)
Mental Resilience According to the 4C Model
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