You are a dedicated data scientist who loves working with people across technical and non-technical domains to solve problems and deliver results.
In this role, you will build models to detect anomalies, forecast outcomes, and apply causal inference to connect system changes to customer and business impact.
You will design and analyze experiments to guide product and engineering decisions, partner across product, engineering, and operations to embed data-driven thinking into roadmaps, and apply advanced methods — including NLP, LLMs, and GenAI where valuable — to improve diagnostics and partner self-service.
Most importantly, you will turn complex data into clear, actionable insights that shape how Apple Pay and Wallet evolve at global scale.
3-5 years of experience in data science, business analytics, or BI.
Ability to extract meaningful business insights from data and identify the stories behind the patterns.
Understanding of statistical concepts and practical experience applying them (in anomaly detection, forecasting, causal inference, NLP etc.).
Expert level SQL with advanced Snowflake performance tuning expertise.
Python development experience with pandas and NumPy.
Communication skills for presenting complex quantitative analyses to senior business executives.
Bachelor's degree required in quantitative field (Data Science, Applied Econometrics, Statistics, Machine Learning, Analytics, Mathematics, Operations Research, or related).
Strong critical thinking and interpersonal skills with the ability and desire to learn and evaluate new technologies.
Self-directed and proactive. You thrive in an ambiguous and fast-paced environment.
Advanced degree (Master’s/PhD) preferred in quantitative field (Data Science, Applied Econometrics, Statistics, Machine Learning, Analytics, Mathematics, Operations Research, or related field).
Proven understanding of machine learning, deep learning and natural language (including LLMs) processing and ability to optimize machine learning models to adapt to solving various kinds of issues.