Data Scientist II

Data Scientist II

San Diego, CA (Hybrid)

Overview:

The person in this role will be joining the Decision Science team, which is a part of the larger Data Science, Analytics, & Enablement (DSAE) organization here at SIE. Using data, you will play a crucial role in driving decision-making and delivering impactful insights to our stakeholders. You will be responsible for applying advanced analytical techniques, building Machine Learning (ML) models, and using 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 analyze large and complex datasets, extracting actionable insights in a commercial setting.

• Apply your knowledge in 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 these hypotheses.

• Independently extract and integrate data from multiple data systems (including writing complex SQL queries) to support analytical efforts.

• Participate and support in delivering results and presentations to stakeholders.

• Promote and share the team's work, methods, and skills in Data Science, Causal Inference, and ML with team members and the broader community.

• Stay up to date on the latest advancements in the field of 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. BA/BS Degree could be considered based on 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 Causal Inference methods (such as propensity score matching, synthetic control methods, and difference-in-differences) for accurately estimating causal relationships.

• Proficiency in designing and performing hypothesis tests to validate or reject research hypotheses.

• Knowledge of the gaming industry and relevant gaming titles is a plus.

• Familiarity with Gen AI tools and pipelines is a plus.

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