Data Scientist

Imagine what you can do here. Apple is a place where extraordinary people gather to do their lives best work. Together we create products and experiences people once couldn't have imagined, and now, can't imagine living without. It's the diversity of those people and their ideas that inspires the innovation that runs through everything we do.

Description

APPLE INC has the following available in Cupertino, California and various unanticipated locations throughout the USA. Develop and implement a set of techniques or analytics applications to transform raw data into meaningful information using data-oriented programming languages and visualization software. Apply data mining, data modeling, natural language processing, and machine learning to extract and analyze information from large structured and unstructured datasets. Create software to analyze large data sets and provide insights to executives, Software Engineers and Managers to help make product decisions and improve Apple's products and services, using Python, visualization tools like Tableau, and presentation tools like Keynote and Jupyter Notebooks. Analyze operating system feature usage data from customers who opt-in to share data with Apple at scale with advanced data science techniques including classification, regression, clustering, and association analysis, after querying the data using SQL or Spark jobs Develop big data analytics platform solutions in Python for code reuse for self and peer data scientists at Apple. Complete statistical sampling and relevance studies in Python by computing significance errors to ensure accurate portrayal of. Responsible for feature selection analytics for large longitudinal studies in Python (e.g., Wrapper methods (forward, backward, and stepwise selection), Filter methods (ANOVA, Pearson correlation, variance thresholding), and Embedded methods (Lasso, Ridge, Decision Tree)) for accurate advanced data science analysis Access data via Python in Structured Query Language (SQL) and Hadoop Distributed File System (HDFS) environments to achieve all of the above. Test new OS features before they release to ensure the data collected matches expectations. 40 hours/week. At Apple, base pay is one part of our total compensation package and is determined within a range. This provides the opportunity to progress as you grow and develop within a role. The base pay range for this role is between $167,946 - $205,500/yr and your base pay will depend on your skills, qualifications, experience, and location. PAY & BENEFITS: Apple employees also have the opportunity to become an Apple shareholder through participation in Apple's discretionary employee stock programs. Apple employees are eligible for discretionary restricted stock unit awards, and can purchase Apple stock at a discount if voluntarily participating in Apple's Employee Stock Purchase Plan. You'll also receive benefits including: Comprehensive medical and dental coverage, retirement benefits, a range of discounted products and free services, and for formal education related to advancing your career at Apple, reimbursement for certain educational expenses - including tuition. Additionally, this role might be eligible for discretionary bonuses or commission payments as well as relocation. Learn more about Apple Benefits: https://www.apple.com/careers/us/benefits.html. Note: Apple benefit, compensation and employee stock programs are subject to eligibility requirements and other terms of the applicable plan or program.

• Master's degree or foreign equivalent in Data Science, Computer Science, Business Analytics or related field and 2 years of experience in the job offered or a related occupation.
• 2 years of experience with each of the following skills is required:
• Utilizing Python for scaled code development and data science framework
• Utilizing Hadoop commands on the command line for distributed machine cluster data
• Analyzing large data sets using Spark
• Utilizing data visualization, Tableau to review and report scale data
• Presenting statistic models to software engineers and managers
• Utilizing Feature Selection algorithms in Python, Wrapper methods (forward, backward, and stepwise selection), Filter methods (ANOVA, Pearson correlation, variance thresholding), and Embedded methods (Lasso, Ridge, Decision Tree) to increase the algorithms prediction
• Gathering data for analysis by using SQL
• Writing high-quality machine learning models and algorithms using statistical analysis concepts
• Utilizing programming language Python for Data wrangling
• Utilizing database management tools such as mySQL and P4ostgreSQL for data extraction, analysis and data warehouse system

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