Senior ML Data Scientist, Apple Pay Analytics

Summary

Apple is where individual imaginations gather together, committing to the values that lead to great work. Every new product we build, service we create, or Apple Store experience we deliver is the result of us making each other’s ideas stronger. That happens because every one of us shares a belief that we can make something wonderful and share it with the world, changing lives for the better.

It’s the diversity of our people and their thinking that inspires the innovation that runs through everything we do. When we bring everybody in, we can do the best work of our lives. Here, you’ll do more than join something — you’ll add something. At Apple, extraordinary ideas have a way of becoming great products, services, and customer experiences very quickly.

If you are an ambitious, hands on seasoned senior analytics, high-energy individual who is not afraid of challenges, we're looking for you to join the Wallet, Payments & Commerce Analytics organization to craft the future for the Apple Pay business.

About the role

Your recommendations will directly shape decisions at the highest levels, while you partner with the extended data sciences & data engineering to ensure reliable, accessible data. We work fast and iteratively, value open feedback and debate, and balance teamwork with independent decision-making and smart risk-taking.

Responsibilities

• In this role, you’ll be a strategic partner to senior leaders, driving high-impact initiatives across multiple functions.
• You’ll turn complex business challenges into clear analytical approaches, using advanced techniques to uncover insights, measure performance, and identify opportunities for growth and efficiency.
• Develop time series forecasting models to predict critical business KPIs, revenue trends, and adoption patterns to support strategic planning and resource allocation.
• Apply machine learning techniques to understand business growth drivers, identify emerging trends, and quantify the impact of key business initiatives.
• Build analytical frameworks that combine statistical modeling and ML methods to deliver forward-looking insights to executive leadership.
• Translate complex forecasting results and ML-driven analyses into clear business narratives that inform strategic decisions.
• Leverage, build GenAI applications to automate insights generation, enhance data quality processes, and devise new capabilities such as intelligent anomaly detection.
• Design advanced ML models and capabilities to automate A/B test result interpretation, generate experiment summary narratives, and build intelligent systems that recommend optimal test designs.

Minimum Qualifications

• 5+ years of demonstrated experience in a Machine Learning Engineer role.
• Strong business acumen and the ability to think strategically and operationally.
• Experience with time series forecasting methods and ML techniques for business analysis (regression, classification, clustering) to solve strategic business problems.
• Be a self-starter, driven, accountable and a high-energy team player.
• Proven experience being a thought partner to cross-functional business teams offering actionable data insights and recommendations.
• Demonstrated ability to influence without authority.
• Excellent communication skills—able to distill complex analysis into simple, compelling narratives. Background in consulting or customer-facing, fast-paced analytical environment.
• Expertise with SQL, R or Python and data visualization tools such as Tableau for full-stack data analysis, insight synthesis and presentation.
• Knowledge of and experience in leveraging Applied Statistical/ML techniques.
• Bachelor’s degree in Economics, Finance, Statistics, Mathematics, Econometrics, or Applied Social Sciences.

Preferred Qualifications

• Demonstrated experience in an Analytics and Strategy role, preferably in the payments/fintech domain
• Ability to understand ambiguous and complex problems and design and execute analytical approaches and turn analysis into clear and concise takeaways that drive action
• Track record of leading organization-wide strategic initiatives or transformative analytics projects
• Master’s degree in Economics, Finance, Statistics, Mathematics, Econometrics, or Applied Social Sciences.