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Apple

Decision Scientist

ChinaPosted 23 days ago

Apply on company site View full job

Why you should apply for a job to Apple:

Ranked as one of the Best Technology Companies in 2020
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65% would recommend this company to other women
66% say the CEO supports gender diversity

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Imagine what you could do here! The people here at Apple don't just create products - they build the kind of wonder that's revolutionized entire industries. It's the diversity of those people and their ideas that inspires the innovation that runs through everything we do, from amazing technology to industry-leading environmental efforts. At Apple, inclusion is a shared responsibility, and we work together to foster a culture where everyone belongs and is inspired to do their best work.

Do you have a strong passion for using data to drive business decisions, generate ideas, and inspire collaborators? As a Decision Scientist on the Retail Store Analytics team, you will play an integral role in helping Apple Retail make decisions using data. Design, refine, and create innovative analyses. Leverage large and complex data sources from across Apple to deliver data products.

Description
You will partner with cross-functional teams to identify, frame, prioritize, and answer business questions where analytics can be most impactful. Design and implement analytics and reporting tools to support key decisions on a wide variety of topics including product launches, customer experience, and operational performance. Other responsibilities include the following:
Design, create, refine, and maintain analyses and reporting.
Collaborate with other decision scientists, analysts, and engineers to build full-service data solutions.
Perform all aspects of analysis including data manipulation, programming, data visualization, documentation, and presentation of results.
Develop a deep understanding of our retail customer base and purchase choices and contribute to the development of tools to improve business efficiency and productivity.","responsibilities":"Analyze large volumes of data; extract and manipulate large datasets using tools such as SQL, dbt, command line and scripting languages.
Analyze data covering a wide range of information from financial reporting to device purchase patterns such as export and trader activity and identify new patterns through data mining.
Design, develop, validate and maintain ad hoc as well as foundational performance analyses and business reporting
Use data observability tools to develop and manage and automate testing, alerting and improve data quality.
Collaborate with data and business stakeholders to translate business needs into project requirements and communicate complex concepts and the results of the analyses in a clear and effective manner to business partners.
Strong collaborative technical experience, including using version control, release management, requirements gathering, data testing and documentation.

Preferred Qualifications
2+ years of experience performing machine learning and predictive modeling
Experience translating business needs into logic and key performance indicators
Experience with BI processes and some experience with dashboard tools like Tableau
Master's degree in a relevant field (Engineering, Data Science, Business) or equivalent experience

Minimum Qualifications
4+ years of related work experience in decision/data science, consulting, business analytics, or other related business facing role
Bachelor's degree in a relevant field (Engineering, Data Science, Business) or equivalent experience
4+ years experience coding in SQL and Python
4+ years experience performing rigorous statistical analysis
Ability to initiate, refine, and complete projects with minimal guidance
Ability to think critically and and collaborate cross-functionally with other analytics and business stakeholders to distill business questions into concise analyses and reports.

About Apple

Working at Apple means doing more than you ever thought possible and having more impact than you ever imagined.

Size: 10,000+ employees
Industry: Technology: Manufacturing

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