🏢 Amazon is hiring a Data Scientist, Amazon Music - DISCO
Location: 🇺🇸 Culver City, California, United States
💲 Salary: $97500 - $185000

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https://datasciencejobs.com/jobs/data-scientist-disco-amazon-united-states-58/

🏢 esure Group is hiring a Lead Data Scientist
Location: 🇬🇧 Reigate, Surrey, United Kingdom

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https://datasciencejobs.com/jobs/lead-data-scientist-esure-group-united-kingdom-2/

🏢 American Express is hiring a Manager-Data Science
Location: 🇺🇸 New York, New York, United States
💰 Salary: $90000 - $165000

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https://datasciencejobs.com/jobs/data-scientist-american-express-united-states-27/

💼 Wpromote is hiring a Director, AI Solutions
Location: 🇺🇸 Los Angeles, California, United States
💲 Salary: $135000 - $175000

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https://datasciencejobs.com/jobs/director-ai-solutions-wpromote-united-states-2/

🏢 Sana Commerce is hiring a Data Engineer
Location: 🇳🇱 Rotterdam, Netherlands

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https://datasciencejobs.com/jobs/data-engineer-sana-commerce-netherlands-2/

🏢 Microsoft is hiring a Data Solution Engineer
Location: 🇯🇵 Tokyo, Tokyo-to, Japan

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https://datasciencejobs.com/jobs/data-solution-engineer-microsoft-japan-76/

🏢 AT&T is hiring a Manager-Data/AI Engineering
Location: 🇮🇳 Hyderabad, Andhra Pradesh, India

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https://datasciencejobs.com/jobs/manager-dataai-engineering-at-t-india-6/

Reinforcement Learning doesn’t tell you what’s right.
It only tells you how good your choice was.
No feedback on what to do. Only on how it went.

 Example: A multi-armed bandit (like a slot machine with several levers). You don't know which lever is the best - you can only find out by trying it out. Exploring means giving up a known reward (from exploitation) — in hopes of finding a better one.

This balance between exploration and exploitation is the central dilemma in reinforcement learning.

 A simple strategy is ε-greedy:
→ In 90% of cases you take the best known action
→ In 10% of cases, you try a different one by chance

In simulations, ε-greedy methods perform better in the long term than pure greed (always take the supposedly best) - because they master the “explore-exploit trade-off”.

#ReinforcementLearning #ML #KI #AI #DataScience #MachineLearning #Datascientist