I am a Data Scientist at King.com.
I've been working in game dev for 6+ years on a lot of well-known mobile titles (such as Cut the Rope, Candy Crush Soda etc). I'm passionate about bringing data perspective to improve users' experience.
As a Data Scientist I'm interested in topics such as segmentation and personalisation, explainable ML models, A/B testing, optimization, and Bayesian statistics.
Live stream: https://www.youtube.com/watch?v=0a0c-aMj1Xs
Optimization libraries such as SciPy or Nevergrad are commonly used in different data science workflows, such as choosing optimal hyperparameters for a machine learning model or taking actions based on forecasts. In this presentation, we will discuss how such an optimizer can be used to build reward configurations for games (by rewards configurations here we mean bundles of different in-game items that players may get for completing different tasks/quests in a game) Using rewards in Candy Crush Soda as an example, I will show how the problem can be solved using the Nevergard library from Facebook.