WPC Apple Cash Data Scientist

Apple • Cary, NC • 6h ago

Weekly Hours: 40

Role Number: 200653784-1435

Summary

Apple is a place where extraordinary people gather to do their best work. Together we craft products and experiences people once couldn't have envisioned - and now can't imagine living without. If you're excited by the idea of making a real impact and joining a team where we pride ourselves in being one of the most diverse and expansive companies in the world, a career with Apple might be your perfect job.

The Wallets, Payments, and Commerce (WPC) team at Apple is looking for a full-stack Data Scientist who is passionate about crafting and implementing data solutions that have a direct and measurable impact on Apple customers. You will employ predictive modeling and statistical analysis to build end-to-end solutions for improving the adoption of Apple wallet and Apple Cash. You will be a thought partner to the business, understand strategic goals, and then use your skills and subject matter expertise to surface impactful insights that drive business decisions and customer benefits. You will collaborate with partners across product, design, engineering, and business teams to drive your recommendations into action. Our culture is about getting things done iteratively and rapidly, with open feedback and debate along the way. We believe Data Science is a team sport, but we strive for independent decision-making and taking smart risks.

Description

Take deep dives in large-scale data to identify key insights that will shape future product strategy Collaborate with cross-functional teams to identify new growth opportunities, develop data requirements, establish product critical metrics, and evangelize data products Design, deploy, and evaluate measurement plans and feature launch reporting that help define opportunities for higher adoption, improved business performance, and better customer experience Conduct hypothesis-driven exploratory analyses, select appropriate ML algorithms, and build complex optimization engines to deliver impactful data solutions Research new technologies and methods across data science and data engineering to improve the technical capabilities of the team Communicate insights to senior management by distilling complex analysis and concepts into concise business-focused takeaways

Minimum Qualifications

5+ years of Python (e.g., Pandas, Polars) with experience working with relational databases, including SQL, and large-scale distributed systems such as Redshift

Expert in designing and implementing end-to-end descriptive data pipelines and business reports that enable data-driven decisions

Practical experience with and theoretical understanding of ML algorithms for classification, regression, clustering, anomaly detection, and casual inference frameworks

Proven ability to extract meaningful business insights, identify root causes behind trends, and recommend actionable strategies

Exceptional data visualization and storytelling abilities, capable of translating complex analyses into clear, executive-ready insights

Excellent written and verbal communication skills, adept at translating technical results into clear, compelling narratives for non-technical and executive audiences

Highly organized, self-driven, and effective at prioritizing and delivering multiple initiatives under tight timelines

Bachelor's degree in engineering, economics, statistics, computer science, or related quantitative field or related experience

Preferred Qualifications

Advanced degree in Applied Econometrics, Statistics, Data Mining, Machine Learning, Analytics, Mathematics, Operations Research, Industrial Engineering, or related field preferred

Apple is an equal opportunity employer that is committed to inclusion and diversity. We seek to promote equal opportunity for all applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, Veteran status, or other legally protected characteristics. Learn more about your EEO rights as an applicant (https://www.eeoc.gov/sites/default/files/2023-06/22-088\EEOC\KnowYourRights6.12ScreenRdr.pdf) .

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