Select your current position

Pick a role and level — we'll show the growth path, skills and gap analysis.

Development path

Junior

0-2 years

Current

Responsibility: Exploratory Data Analysis (EDA). Building baseline models. Feature engineering. Data visualization. Preparing reports.

Key skills:

Gradient Boosting Need
ML Pipelines Need
Algorithms & Complexity Need
Data Structures Need

Middle

2-5 years

Next

Responsibility: Formalizing business problems as ML tasks. Building and validating models. A/B testing. Presenting results to stakeholders.

Key skills:

Gradient Boosting Need
ML Pipelines Need
Algorithms & Complexity Need
Data Structures Need

Senior

5-8 years

Responsibility: Researching new approaches (NLP, CV, RecSys). Designing experiments. Publishing results. Mentoring. Cross-functional collaboration.

Key skills:

Apache Spark Need
AWS Need
BI Dashboards Need
Classical ML (scikit-learn) Need
ClickHouse Need
Code Review Need
Data Quality Need
Docker Need
Feature Stores Need
Gradient Boosting Need
LLM Applications Need
ML Pipelines Need
MLflow Need
Model Serving Need
Pandas / Polars Need
PostgreSQL Need
Python Web Frameworks Need
PyTorch Need
REST API Design Need
SQL-based ETL Need
Transformers & NLP Need
Unit Testing Need
Algorithms & Complexity Need
Database Indexing Need
Code Quality & Refactoring Need
Model Monitoring Need
Query Optimization Need
Recommender Systems Fundamentals Need
OOP & SOLID Principles Need
Data Structures Need
Experiment Tracking Need

Lead / Staff

7-12 years

Responsibility: Data Science strategy. Prioritizing ML projects by business impact. Coordinating DS and Engineering. Experimentation standards.

Key skills:

Apache Spark Need
AWS Need
BI Dashboards Need
Classical ML (scikit-learn) Need
ClickHouse Need
Code Review Need
Data Quality Need
Docker Need
Feature Stores Need
Gradient Boosting Need
LLM Applications Need
ML Pipelines Need
MLflow Need
Model Serving Need
Pandas / Polars Need
PostgreSQL Need
Python Web Frameworks Need
PyTorch Need
REST API Design Need
SQL-based ETL Need
Transformers & NLP Need
Unit Testing Need
Algorithms & Complexity Need
Database Indexing Need
Code Quality & Refactoring Need
Model Monitoring Need
Query Optimization Need
Recommender Systems Fundamentals Need
OOP & SOLID Principles Need
Data Structures Need
Experiment Tracking Need

Principal

10+ years

Responsibility: AI research strategy. Conference publications. Building DS culture. LLM/GenAI adoption strategy.

Key skills:

Apache Spark Need
AWS Need
BI Dashboards Need
Classical ML (scikit-learn) Need
ClickHouse Need
Code Review Need
Data Quality Need
Docker Need
Feature Stores Need
Gradient Boosting Need
LLM Applications Need
ML Pipelines Need
MLflow Need
Model Serving Need
Pandas / Polars Need
PostgreSQL Need
Python Web Frameworks Need
PyTorch Need
REST API Design Need
SQL-based ETL Need
Transformers & NLP Need
Unit Testing Need
Algorithms & Complexity Need
Database Indexing Need
Code Quality & Refactoring Need
Model Monitoring Need
Query Optimization Need
Recommender Systems Fundamentals Need
OOP & SOLID Principles Need
Data Structures Need
Experiment Tracking Need

Gap analysis: skills to develop

To reach the next level you'll need to develop:

Gradient Boosting

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.

ML Pipelines

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.

Algorithms & Complexity

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.

Data Structures

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.

Career transitions

Possible career trajectories for the <strong>Data Scientist</strong> role

↔️ Lateral 1

Adjacent roles for a lateral move

ML Engineer Lateral

╨Я╨╡╤А╨╡╤Е╨╛╨┤ ╨▓ ML Engineering ╤З╨╡╤А╨╡╨╖ production ML

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