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Apple AIML - ML Data Scientist Safety & Red Teaming United States, Pennsylvania, Pittsburgh 226162710
13.08.2024
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Description
- Create evaluation data sets to solve difficult, non-routine analysis problems; applying sophisticated analytical methods as needed- Conduct analyses that includes data gathering and requirements specification, processing, analysis, ongoing work, and presentations- Build and prototype analysis pipelines iteratively to provide insights at large-scale- Develop extensive knowledge of data structures and metrics, advocating for changes where needed for product development
Minimum Qualifications
- Strong programming skills: Including data-querying skills (SQL and/or Spark, etc.) and experience with a scripting language for data processing and development (e.g., Python, R, or Scala)
- Proficiency in data science, machine learning, and analytics: Including statistical data analysis and A/B testing. Experience crafting, conducting, analyzing, and interpreting experiments and investigations
- Experience articulating and answering business questions by applying statistical techniques to available data sources
- Strong interpersonal skills: Proven ability to explain difficult technical topics (esp. causal topics) to everyone from data scientists to engineers to business partners.
Preferred Qualifications
- History driving projects of varying sizes and scopes - some will take months and some weeks — and you will need to cut through ambiguity and know when to dive deep
- Advanced degree or equivalent experience in a quantitative field such as Statistics, Operations Research, Bioinformatics, Economics, Psychology, Computer Science, Sociology, Mathematics, Physics, or similar field
- Proven track record of contributing to diverse teams in a collaborative environment
- A passion for building outstanding and innovative products. This position involves a wide variety of interdisciplinary skills
Additional Requirements
Apple is an equal opportunity employer that is committed to inclusion and diversity. We take affirmative action to ensure equal opportunity for all applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, Veteran status, or other legally protected characteristics.
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