Build and deploy machine learning models and time-series forecasting methods to address strategic business problems in payments and fintech.
Translate complex analyses into clear, actionable insights and narratives for cross-functional partners.
Collaborate with cross-functional teams to influence product, marketing, and business decisions with data-driven recommendations.
Maintain a rigorous approach to data quality, reproducibility, and scalable analytics.
5+ years in a Machine Learning Engineer role.

Key requirements

Build and deploy machine learning models and time-series forecasting methods to address strategic business problems in payments and fintech.
Translate complex analyses into clear, actionable insights and narratives for cross-functional partners.
Collaborate with cross-functional teams to influence product, marketing, and business decisions with data-driven recommendations.
Maintain a rigorous approach to data quality, reproducibility, and scalable analytics.
5+ years in a Machine Learning Engineer role.

Jobs at Apple

20 active roles from this employer in the JobMatcher catalog.

All company jobs

Jobs at Apple

20 active roles from this employer in the JobMatcher catalog.

All company jobs

About the role

Senior ML Data Scientist – Apple Pay Analytics (On-site in Cupertino, California, United States)

Description: Join Apple’s Wallet, Payments & Commerce Analytics organization as a hands-on, senior analytics professional to shape the future of the Apple Pay business. Your recommendations will directly influence decisions at the highest levels as you partner with data science and data engineering teams to ensure data reliability and accessibility. Apple values fast, iterative work, open feedback, healthy debate, and a balance between collaboration and individual decision-making.

Responsibilities

Qualifications: Minimum:

Build and deploy machine learning models and time-series forecasting methods to address strategic business problems in payments and fintech.
Translate complex analyses into clear, actionable insights and narratives for cross-functional partners.
Collaborate with cross-functional teams to influence product, marketing, and business decisions with data-driven recommendations.
Maintain a rigorous approach to data quality, reproducibility, and scalable analytics.
5+ years in a Machine Learning Engineer role.
Strong business acumen with strategic and operational thinking.
Experience with time-series forecasting and ML techniques (regression, classification, clustering) for business analysis.
Self-starter, driven, accountable, and a high-energy team player.
Proven ability to partner with cross-functional teams to deliver actionable data insights and recommendations.
Ability to influence without authority.
Excellent communication skills; abili

About the role

Senior ML Data Scientist – Apple Pay Analytics (On-site in Cupertino, California, United States)

Description: Join Apple’s Wallet, Payments & Commerce Analytics organization as a hands-on, senior analytics professional to shape the future of the Apple Pay business. Your recommendations will directly influence decisions at the highest levels as you partner with data science and data engineering teams to ensure data reliability and accessibility. Apple values fast, iterative work, open feedback, healthy debate, and a balance between collaboration and individual decision-making.

Responsibilities

Qualifications: Minimum:

Build and deploy machine learning models and time-series forecasting methods to address strategic business problems in payments and fintech.
Translate complex analyses into clear, actionable insights and narratives for cross-functional partners.
Collaborate with cross-functional teams to influence product, marketing, and business decisions with data-driven recommendations.
Maintain a rigorous approach to data quality, reproducibility, and scalable analytics.
5+ years in a Machine Learning Engineer role.
Strong business acumen with strategic and operational thinking.
Experience with time-series forecasting and ML techniques (regression, classification, clustering) for business analysis.
Self-starter, driven, accountable, and a high-energy team player.
Proven ability to partner with cross-functional teams to deliver actionable data insights and recommendations.
Ability to influence without authority.
Excellent communication skills; abili

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