Roles · Data Scientist · Junior
4 core skills, 78 in total. Expectations per skill, and what changes at the next level.
This page lists what a Junior } is expected to know and do, skill by skill. Core skills are the ones a manager and peers assess in a review cycle; the rest count only in self-assessment. Main areas: Programming Fundamentals, Machine Learning & AI.
Grouped by area. The label on the right is the expected depth: Awareness, Working, Advanced or Expert.
Understands basic sorting and search algorithms, evaluates their complexity via Big-O notation. Applies knowledge to analyze data pipeline performance and select appropriate data structures for dataset processing.
Knows core data structures: arrays, hash tables, trees, graphs and their applications in data science. Selects appropriate structures for storing and processing data in pandas DataFrame, numpy arrays, and standard Python collections.
Understands gradient boosting principles and its differences from random forest. Trains XGBoost and LightGBM models on tabular data with basic hyperparameter tuning. Interprets feature importance to explain model results.
Creates basic ML pipelines via scikit-learn Pipeline: preprocessing, feature engineering, model training. Understands pipeline importance for reproducibility and data leakage prevention. Saves pipelines as single artifacts for deployment.
Not assessed by the team, but part of the self-assessment and the development plan.
78 skills get a higher expectation or become core when moving from Junior to Mid-level. The biggest jumps first.
} in the open competency matrix: 78 skills across 5 levels. The matrix is free for individuals and stays free.