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About the Role
As a Data Scientist, you’ll dive into massive streams of player data from globally played games, turning behaviors into insights and machine learning models that directly shape how millions of players experience our games and how our titles perform worldwide. You will work on different projects across various game genres, including life simulation and racing games. Success means taking ownership of a project end-to-end, from problem framing to delivering actionable insights and recommendations independently.
Responsibilities
- Analyze large-scale player data in a fast-paced gaming environment and transform it into clear, actionable insights
- Perform advanced feature engineering using large-scale data from our data warehouse
- Prototype machine learning models to improve existing performance baselines
- Contribute to the development of internal tracking, prediction pipelines, and monitoring systems
- Improve experimental capabilities through robust experimental design and statistical methods, including causal inference and A/B testing
Requirements
- A first full-time professional experience (CDD/CDI) in tech, gaming, or a similar industry is preferred, as the role requires autonomy and the ability to manage day-to-day responsibilities independently
- Proficiency in Python (especially pandas, numpy, scikit-learn, matplotlib, seaborn, etc.)
- Strong SQL skills
- Experience with Git and machine learning workflows, including experimental design and key statistical methods
- Ability to work with large-scale data
- Product- and business-oriented, with the ability to connect data work to real game impact
- Comfortable working and presenting with non-technical stakeholders, with strong ability to simplify and communicate complex topics at a high level
- Able to translate analytical insights into clear, actionable recommendations
Skills
- Python
- pandas
- numpy
- scikit-learn
- matplotlib
- seaborn
- SQL
- Git
- Machine learning workflows
- Experimental design
- Statistical methods
- Causal inference
- A/B testing
Work Type
- Hybrid
Experience Level
- First full-time professional experience
Benefits
- Hybrid work model: 2 days of remote work per week
- Wellpass(ex-Gymlib)
- Group saving plan
- Health coverage
- Lunch vouchers
- Navigo reimbursement 50%
- Flexible working hours
- Unlimited coffee and tea
- Fresh fruits
- Monthly breakfasts
- Dedicated video and board game areas
- Babyfoot
- Ping-pong
- Weekly futsal sessions
- Summer and winter parties
- Regular game or events
About the Company
- We believe great work happens in a great environment.