Roles · Data Scientist · Mid-level
4 core skills, 78 in total. Expectations per skill, and what changes at the next level.
This page lists what a Mid-level } 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.
Analyzes algorithmic complexity of ML pipelines, optimizes bottleneck operations in feature engineering. Understands trade-offs between accuracy and algorithm speed, applies dynamic programming and greedy approaches for computation optimization.
Applies specialized data structures for ML tasks: sparse matrices, KD-trees, bloom filters. Optimizes dataset memory footprint through proper dtype selection and sparse representations. Understands pandas and numpy internal structures.
Independently trains production-ready gradient boosting models with advanced tuning. Works with XGBoost, LightGBM, and CatBoost, selects the optimal framework. Configures early stopping, regularization, and categorical features handling.
Designs production ML pipelines using Airflow, Prefect, or Dagster. Automates the full ML cycle: data ingestion, validation, feature engineering, training, evaluation, deployment. Configures scheduling and retry logic.
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 Mid-level to Senior. The biggest jumps first.
} in the open competency matrix: 78 skills across 5 levels. The matrix is free for individuals and stays free.