Senior Data Scientist

Rovio is part of the SEGA family and world famous for our flagship IP Angry Birds - want to know more about Rovio as an employer? Click here.

You will have impact and fun at work by

• Developing predictive and statistical models that support large-scale User Acquisition decisions.
• Tackling challenging problems involving long-term forecasting, uncertainty quantification, optimization, and rare-event behaviour.
• Exploring and evaluating new modelling approaches and translating research ideas into production systems.
• Collaborating closely with stakeholders across Rovio — including UA managers, game teams, finance, analysts, engineers, and fellow data scientists — to solve high-impact business problems.
• Taking ownership of models throughout their lifecycle, from problem formulation and experimentation to deployment and monitoring.
• Contributing to a highly collaborative modelling culture where problems are scoped together, ideas are workshopped openly, and solutions are developed through pair coding, code reviews, and close day-to-day collaboration with the team.

Experience and skills we are looking for

• You have extensive years of experience in data science.
• You hold an academic degree in applied mathematics, statistics, machine learning, computer science, or a related field.
• You’re proficient in Python, including numerical and data science libraries, and you’ve designed data pipelines in cloud-based environments.
• You have a strong understanding of at least two of the following areas: Bayesian modeling, Statistical inference, Predictive modelling and machine learning, Optimization and decision theory, Uncertainty quantification, Numerical methods.
• You can communicate complex technical concepts to both technical and non-technical teammates, making sure everyone is on the same page.
• Enjoyment of collaborative problem-solving and working closely with other experts.

It would be nice if you also have the following skills

• Experience in mobile gaming, User Acquisition, or digital advertising.
• Experience with experimentation, causal inference, or reinforcement learning.
• Experience building large-scale production ML systems.