Data Product Manager - Wallet & Apple Pay
Santa Clara Valley (Cupertino), California, United States
Software and Services
Looking for a talented, hardworking 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 Apple Pay. Your role will be instrumental as it provides an essential 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. The ideal candidate is a self-motived team player, executes with little mentorship, takes ownership of their work, is detail oriented, is relentless in their pursuit of building a solid data foundation, has strong data intuition and a strong passion for data. Ability to adapt and learn quickly, provide results with limited direction, and communicate complex results effectively is a must. Familiarity with generic big data infrastructure (HDFS, MapReduce, Scala, Spark, Splunk) as well as traditional relational database structures and Python is essential.
- 5+ years of experience in a Data Scientist or Data Analyst role with extensive experience with both client and server tagging/instrumentation concepts & frameworks.
- 5+ years experience in product manager role!
- You have a passion for empirical research and answering hard questions with data; the demonstrated ability to conceptualize, promote and implement new ideas for the business.
- Strong and curious business attitude 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 actionable manner to senior executives. Capable of clearly communicating complex instrumentation needs to a non-technical audience
- Outstanding communication, interpersonal and presentation skills 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.
- Candidates with a background in payments are highly preferred
Define how best to measure and monitor our products and their features. 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. You will build and maintain positive relationships with key partners across the company in order to optimally deliver actionable insights Partnering with other Apple organizations on data gathering, data governance, democratizing data with reporting tools and evangelizing critical metrics. Conducting regular and ad-hoc analyses, data mining and predictive analytics to provide leadership with actionable 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 related field preferred.