Skill Profile

Reinforcement Learning

This skill defines expectations across roles and levels.

Machine Learning & AI Deep Learning

Roles

where this skill appears

Levels

structured growth path

Mandatory requirements

the other 5 optional

Machine Learning & AI

Deep Learning

2/22/2026

Choose your current level and compare expectations. The items below show what to cover to advance to the next level.

What is Expected at Each Level

The table shows how skill depth grows from Junior to Principal. Click a row to see details.

Role	Required	Description
Data Scientist		Understands the fundamentals of Reinforcement Learning. Applies basic practices in daily work. Follows recommendations from the team and documentation.

Role	Required	Description
Data Scientist		Applies RL for business tasks: recommender systems, dynamic pricing, content personalization. Uses PPO, SAC, A2C via stable-baselines3. Designs reward functions for real-world tasks, handles sparse rewards.

Role	Required	Description
Data Scientist		Designs production RL systems: offline RL, contextual bandits, multi-agent RL. Applies model-based RL for data-efficient training. Addresses production RL challenges: safety constraints, online evaluation, sim-to-real transfer.

Role	Required	Description
Data Scientist		Defines RL strategy for the data science team. Establishes guidelines on RL vs supervised learning applicability. Coordinates RL infrastructure development: simulation environments, evaluation frameworks, safety tools.

Role	Required	Description
Data Scientist		Shapes RL strategy at organizational level. Defines investments in RL research and infrastructure. Evaluates cutting-edge approaches: RLHF for LLM, world models, foundation models for RL. Publishes applied RL research.

Junior 1 requirements

Data Scientist

Understands the fundamentals of Reinforcement Learning. Applies basic practices in daily work. Follows recommendations from the team and documentation.

Middle 1 requirements

Data Scientist

Applies RL for business tasks: recommender systems, dynamic pricing, content personalization. Uses PPO, SAC, A2C via stable-baselines3. Designs reward functions for real-world tasks, handles sparse rewards.

Senior 1 requirements

Data Scientist

Designs production RL systems: offline RL, contextual bandits, multi-agent RL. Applies model-based RL for data-efficient training. Addresses production RL challenges: safety constraints, online evaluation, sim-to-real transfer.

Lead / Staff 1 requirements

Data Scientist

Defines RL strategy for the data science team. Establishes guidelines on RL vs supervised learning applicability. Coordinates RL infrastructure development: simulation environments, evaluation frameworks, safety tools.

Principal 1 requirements

Data Scientist

Shapes RL strategy at organizational level. Defines investments in RL research and infrastructure. Evaluates cutting-edge approaches: RLHF for LLM, world models, foundation models for RL. Publishes applied RL research.

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