Data Science Manager, AppleCare Digital

Full time

Employment Information


The AppleCare Digital team is looking for an outstanding Data Science Manager to lead a team of versatile and experienced Data Scientists and Data Engineers. This team is part of the AppleCare Digital Business Insights organization and owns both data science and data engineering for AppleCare Digital. In Data Science, the team is responsible for strengthening our machine learning driven capabilities, developing machine learning roadmap and infrastructure, and all research and development activities for machine learning projects. In Data Engineering, the team is responsible for building analytical data engineering including defining data requirements, identifying and addressing data gaps, building data pipelines, and maintaining reporting and dashboards.

This team’s work is a key enabler for AppleCare Digital to drive value across Service, Sales, and Support, and drive key partnerships with other data science & analytics teams in the broader AppleCare, Marcom and Retail organizations.

The person in this position will partner with other Business Insights teams and areas across AppleCare Digital to help identify, prioritize, and drive end-to-end data science & analytics solutions. This role is based out of Bangalore.

Key Qualifications

  • 8+ years of industry experience in a Data Science & Data Engineering role.
  • 4+ years of experience managing a Data Science & Data Engineering team.
  • Experience navigating through sophisticated organizational structures and influencing business partners and collaborators in decision making.
  • Experience working in diverse multi-functional teams to get results and sharing a point of view and direction on analytics, data science, experimental design, and measurement.
  • Experience articulating and translating business questions to clearly defined problem statements and using data science techniques to answer those questions.
  • Experience and familiarity with common data science tools, such as Python/R and various data processing and machine learning libraries.
  • Experience producing powerful visualizations and dashboards that balance both art and science (using Tableau/D3, etc)


  • Manage a team of hard-working data scientists and data engineers, working on critical and timely digital analytics and data science initiatives for AppleCare.
  • Lead design of business goals driven data science & analytics projects in the areas of customer support and customer behavior analysis using valid scientific techniques.
  • Define and drive overall customer experience analytics including segmentation, service differentiation, operational efficiencies, and resultant customer behavior.
  • Work with business partners and multi-functional teams to define and refine business and research questions and provide leadership to the team to answer those questions.
  • Present findings from research and make recommendations to leadership and multi-functional business partners.
  • Find opportunities for enhancing customer satisfaction scores and reducing customer workload across AppleCare touch points, including call centers, digital and repair centers.
  • Coordinate across multi-functional areas to generate, manage and implement new initiatives, track status, and identify critical issues and their resolution.
  • Conduct end-to-end analyses across all AppleCare touch points, including data gathering from large and complex data sets, data processing, and analyses employing advanced statistical and machine learning methods to improve the quality of analytical delivery.
  • Use Return on Investments (values) to capture financial impacts of initiatives and prioritize initiatives based on return on investment predictions.
  • Partner with peers to build and prototype analytics and data pipelines that provide insights at scale. Evangelize adoption of standard methodologies and build greater awareness of common data analysis pitfalls.

Education & Experience

Advanced degree (MS / MBA / PhD) in a quantitative field such as Data Science, Statistics, Operations Research, Engineering, Computer Science, Information Technology, Mathematics, Physics, Finance, Economics or a similar quantitative field.


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