WPC Apple Cash Data Scientist

Apple Inc.
Cary, United States of America
7 days ago

Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Working hours
Regular working hours
Languages
English
Experience level
Senior

Job location

Cary, United States of America

Tech stack

Data analysis
Cluster Analysis
Information Engineering
Data Mining
Data Systems
Data Visualization
Relational Databases
Distributed Systems
Python
Machine Learning
SQL Databases
Pandas
Information Technology
Data Analytics
Data Pipelines

Job 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","responsibilities":"

Requirements

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

About the company

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.

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