Data Product Manager - Wallet & Apple Pay - NYC
- New York, NY
Posted: Oct 6, 2020
Role Number: 200197667
Looking for a hardworking, passionate and results-oriented individual to join our team and play a key role leading data product management and partnering w/ data scientists and data analysts as we deliver new insights to craft the future of Wallet & Apple Pay. Your role will be instrumental as it provides a crucial foundation on which analytics will be performed. 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. Our team collaborates deeply with partners across product and design, engineering, and business teams: our mission is to drive innovation through deep quantitive analysis. Reporting to the head of Wallet Payments & Commerce Data Engineering & BI, this person will be responsible for client instrumentation/tagging of product features, design & implementation of required data structures, data induction, and reporting & analytics for key commerce and payments projects including Apple Pay. In this role you will work closely with engineering and business as you drive data requirements and manage the data lifecycle ensuring all data needs are met to deliver groundbreaking insights for feature development and business insights.
- Demonstrated ability in a Data Scientist or Data Analyst role with extensive experience with both client and server tagging/instrumentation concepts & frameworks.
- Proven experience in a product manager role
- Deep expertise with analyzing digital commerce flows/pathing
- Have a passion for empirical research and answering hard questions with data; the demonstrated ability to conceptualize, promote and implement new insights for the business.
- Strong and curious business mentality with an ability to condense complex concepts and analysis into clear and concise takeaways that drive action, with minimal mentorship.
- Have strong writing, and communication skills with the ability to communicate complex quantitative requirements to engineering in a clear, detailed, and measurable manner to senior executives. Capable of clearly communicating complex instrumentation needs to a non-technical audience
- Outstanding communication, interpersonal and presentation abilities with meticulous attention to detail.
- Expertise in crafting SQL friendly data structures and implementing complex SQL queries.
- Exposure to data Quality Assurance, large volume data sets and Big Data technology stack such as Hadoop, Hive and Spark preferred.
- Familiarity with Python or R, Splunk and data visualization tools such as Tableau for full-stack data analysis, insight synthesis and presentation.
- Comfortable learning new technologies and working in a fast-paced environment
- Candidates with a background in payments are highly preferred
Engage with the business, engineering, product management teams as a thought partner Lead all instrumentation/tagging efforts for commerce within Wallet & Apple Pay Dive deep into the large-scale data to identify key insights that will inform product improvements and business strategy. Partner with other Apple organizations on data gathering, data governance, redefining data with reporting tools and evangelizing critical metrics. Conducting regular and ad-hoc analyses, data mining and predictive analytics to provide leadership with measurable insights at tactical and strategic levels related to product and service usage and experience. Design and develop highly polished and functional Tableau dashboards to support pathing analytics and product/feature usage, and work with data engineering teams on designs for higher level self service tools. Quickly outline and build customized analyses for leadership
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 equivalent preferred.
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