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Apple Data Scientist — Wallet Payments & Commerce United States, West Virginia 112627217
04.09.2025
- 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.
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