Hardware Insights Data Scientist

Job Overview

Key Qualifications

5+ years experience as a data analyst or data scientist

Experience with the hardware feedback loop and voice of the customer analysis

High proficiency with SQL and relational databases such as Teradata

High proficiency experience with Tableau visualization, including large scale dashboard deployment and maintenance

Experience with Excel for pivot table analysis

Excellent analytical and problem solving skills

Strong communication skills and comfort presenting to senior leadership

Demonstrated ability working with multiple teams to complete critical achievements under pressure with tight deadlines

Understanding of hardware continuous improvement processes

Ability to work closely with business parts in engineering, operations, hardware and software engineering to improve the product feedback loop

Ability to partner with multiple teams and accomplish tasks under tight deadlines

Experience in the development, training, tuning and delivery of ML models using python / R / Turi

Ability to study and enrich the business via interpretation and continuous improvement of pre-existing ML assets

Description

This role is responsible for the development of KPIs and data assets in support of delivering insights spanning hardware and software field performance for multiple product lines. The focus will be on expanding an already robust hardware feedback loop, with attention to underutilized data sets, identification of opportunities for early warning on changes in customer service volumes, automation of analytic operations, and enhancement of existing insight assets. Particular attention will be focused on data visualization, automation and trend/pattern detection spanning multiple product lines.Responsibilities include:Leading the Voice of the Customer (VOC) feedback loop for AppleCareInteracting with product teams to identify questions and opportunities for data analysis, experiments and insightsDeveloping new performance metrics and data visualization methods that consider the impact of the entire product eco systemDeveloping algorithms for automated processes to cleanse, evaluate and derive patterns from large datasets spanning disparate sourcesIdentifying meaningful insights from large data and metadata sources; interpreting and communicating insights and findings from analysis and experiments to operation, engineering and business management.

Education & Experience

BS/BA degree required, preferably in a quantitative field, such as statistics, engineering, computer science, data analytics or mathematics

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