Data, AI & Analytics
Data Science & Machine Learning skills assessment
Data scientists build models that predict and automate. This scorecard covers problem framing, modelling, evaluation, deployment and ethics for data science teams working with real business data.
- 10 skills to score
- 1–10 scale on every skill
- 5 starting levels
The 10 Data Science & Machine Learning skills you can score
These are the default Data Science & Machine Learning skills in SkillScorer. Each one is scored from 1 to 10 for every analyst, and you can edit, remove or add skills at any time.
Problem Framing and Success Metrics
Turns business problems into well-defined modelling tasks with measurable success criteria.
Exploratory Data Analysis
Explores data thoroughly to uncover structure, issues and useful features.
Feature Engineering and Data Preparation
Creates informative features and handles missing values, leakage and imbalance.
Model Selection and Training
Chooses appropriate algorithms and tunes them sensibly.
Model Evaluation and Validation
Evaluates models rigorously with proper validation and honest metrics.
Coding and Reproducibility
Writes clean, versioned, reproducible code in Python or R.
Deployment and Monitoring Awareness
Works with engineers to deploy models and monitor drift and performance.
Explainability and Communication
Explains model behaviour and limits to non-technical stakeholders.
Bias, Fairness and Responsible AI
Tests for bias and considers ethical impact and legal obligations.
Experiment Design and A/B Testing
Designs and interprets experiments correctly.
Levels included with Data Science & Machine Learning
Data Science & Machine Learning starts with 5 levels, which SkillScorer calls “Seniority Level” for data, ai & analytics, so analysts can be grouped and compared fairly. Rename, reorder or replace them at any time.
- Junior Data Scientist
- Data Scientist
- Senior Data Scientist
- Lead
- Head of Data Science
How Data Science & Machine Learning works in SkillScorer
Score
Each analyst in a team is scored on the Data Science & Machine Learning skills in the Skills Matrix: tap a cell, type a score, and it saves itself.
Compare
Rank analysts, compare two side by side, or compare your teams against each other to see exactly where the strengths and gaps are.
Track
Scores are kept year after year, so you can see who is improving and which Data Science & Machine Learning skills need attention. Reports also follow each team lead's team.
Data Science & Machine Learning scorecard: common questions
What skills does the Data Science & Machine Learning scorecard include?
SkillScorer's Data Science & Machine Learning scorecard covers 10 skills: Problem Framing and Success Metrics, Exploratory Data Analysis, Feature Engineering and Data Preparation, Model Selection and Training, Model Evaluation and Validation, Coding and Reproducibility, Deployment and Monitoring Awareness, Explainability and Communication, Bias, Fairness and Responsible AI and Experiment Design and A/B Testing. Each one comes with a written description of what it measures, and you can edit, rename, remove or add skills to suit your organisation.
How are analysts scored in Data Science & Machine Learning?
Every analyst is scored from 1 to 10 on each skill (1 = Poor, 5 = Average, 10 = Elite), one team and year at a time. Scores autosave in the Skills Matrix, and the 18 built-in reports turn them into rankings, radar charts, skill gaps and year-on-year trends.
Which levels come with Data Science & Machine Learning?
Data Science & Machine Learning starts with these levels (called “Seniority Level” in the app): Junior Data Scientist, Data Scientist, Senior Data Scientist, Lead and Head of Data Science. They are just a starting point, so you can rename, reorder or replace them.
Can I change the Data Science & Machine Learning skills and wording?
Yes. The skills, levels and labels are all editable per organisation. In data, ai & analytics the app talks about analysts, teams and team leads, and you can change any of those words too.
Score Data Science & Machine Learning with SkillScorer
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