Data Scientist - Payment Commerce Analytics
- Cupertino, CA
Posted: Feb 12, 2020
Role Number: 200150526
At Apple, extraordinary ideas have a way of becoming great products, services, and customer experiences very quickly. If you are an ambitious, high-energy individual who is not afraid of challenges, we're looking for you to join the Wallet, Payments & Commerce Analytics organization to drive optimization analytics for the Apple Services business. The WPC Analytics team supports all commerce, payments & subscription platforms across all of Apple's Services business (App Store, iTunes, Music, Books, TV+, iCloud, Apple Wallet). You are skilled analytically with a strong business sense. You will be a partner to the business, understand their goals and then use your skills and subject matter expertise to surface actionable insights that drive business and customer benefits. You will collaborate with partners across product, design, engineering, and business teams to drive your findings and recommendations into action. Our culture is about getting things done iteratively and rapidly, with open feedback and debate along the way; we believe analytics is a team sport, but we strive for independent decision-making and taking smart risks.
- 5 + years of recent experience in a data science or data analyst role.
- Strong passion for applied empirical analytics, data mining and predictive analytics to provide with actionable insights
- We seek experience measuring UX impact, customer engagement, planning and analyzing AB experiments.
- Confirmed collaboration, communication and story telling skills with ability to adapt and connect across a variety of audiences. This includes strong writing, and data visualization skills with the ability to communicate complex quantitative analysis in a clear, detailed, and actionable manner to senior executives.
- Ability to partner with the data engineering and BI teams to run workflows, requirements and project roadmap to ensure consistent data availability, data quality and data accessibility.
- Excellent time management skills to operate with tight deadlines and balance the pressure of product launches and executive requests.
- Be a self-starter, driven, accountable and a high-energy teammate
- Strong familiarity with multiple platforms, tools, methodologies in data analysis and insight synthesis.
- Have strong working knowledge of database structures and data warehousing principles and have authoritative level SQL with the ability to ETL both structured and unstructured data from various sources.
- Conceive and execute the design of end to end scripted analytics solutions using SQL/TeraData as well as modern analytical systems in Spark SQL, PySpark/Hadoop as well as experience using Notebooks.
- Experience in data visualization tools such as Tableau for full-stack data analysis, insight synthesis and presentation.
- Prior experience working with financial products or large multinational marketplaces/e-commerce desirable!
You will play a key role improving the AMP commerce, payments & subscription platform. As a member of this team you will help optimize the platform by developing new data products and tuning existing features. A few areas your work will influence include account and payment creation flows, transaction efficiency and authorizations, and subscription management and renewals. Your day to day activities will include: Deep dives in large-scale data to identify key insights that inform product improvements and business strategy. Supervised and unsupervised learning. A/B testing and causal modeling. Define how best to measure and monitor commerce products and features. Engage with business, engineering, product management teams as a thought partner. Build and maintain positive relationships with key partners across the company to successfully deliver actionable insights. Partner with other Apple organizations on data gathering, data governance, democratizing data with reporting tools and evangelizing critical metrics.
Education & Experience
Minimum of bachelor's degree, preferably in economics, statistics, computer science, or related quantitative field. Advanced degree in Applied Econometrics, Statistics, Data Mining, Machine Learning, Analytics, Mathematics, Operations Research, Industrial Engineering, or related field preferred.
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