Data Scientist II - Decision Science

Using data, you will play a crucial role in driving decision-making and delivering impactful insights to our stakeholders. You will apply advanced analytical techniques, build Machine Learning (ML) models, and use experimentation and causal inference methods to solve sophisticated business problems related to platform engagement, product features, and games.

What you’ll be doing:

• Apply advanced statistical and ML techniques to analyse large and complex datasets, extracting actionable insights in a commercial setting.
• Apply your knowledge of Experimentation, Causal Inference, ML, and statistical modelling to develop robust predictive models and identify causal relationships.
• Collaborate with cross-functional teams to define business problems, formulate hypotheses, and design experiments to test them.
• Independently extract and integrate data from multiple data systems, including writing complex SQL queries, to support analytical efforts.
• Support the delivery of results and presentations to stakeholders, translating complex analyses into clear and actionable recommendations.
• Stay up to date on the latest advancements in Data Science, including Causal Inference, Gen AI, and ML, and share knowledge and new ideas with the team.

What we’re looking for:

• Master's Degree or Ph.D. in Applied Math, Economics, Statistics, Engineering, or a related quantitative field, plus at least 2 years of relevant industry experience, ideally in entertainment, gaming, technology, or a related consumer-facing industry. A BA/BS Degree may also be considered based on additional relevant industry experience.
• Advanced knowledge and experience applying ML methods using Python and SQL.
• Familiarity with common development tools and practices, including version control systems (e.g., Git) and workflow management tools (e.g., Airflow).
• Excellent communication and data storytelling skills, with the ability to effectively communicate technical concepts to non-technical partners.
• In-depth understanding and experience using supervised and unsupervised ML techniques.
• Understanding of Online Experimentation (large-scale A/B tests) and Causal Inference methods is a plus
• Knowledge of the gaming industry and relevant gaming titles is a plus.
• Familiarity with Gen AI tools and pipelines is a plus.