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 couldnt have imagined, and now, cant imagine living without. Its the diversity of those people and their ideas that inspires the innovation that runs through everything we do. APPLE INC has the following available in Seattle , Washington. Perform statistical analysis of retail online data with the purpose of optimization (A/B testing) and forecasting. Build, test, implement and analyze multivariate testing programs for the Apple Online Store. Quantify and analyze testing outcomes and provide analytical readouts on test results using various statistical techniques. Work with large and complex data sets and partner with Data Engineering teams to ensure accurate collection of data values into the analytical clickstream. Design and execute observational and experimental studies of causal inference to drive feature evaluation and product roadmap with data science based insights. Develop scalable data models and solutions to be used to drive analyses, reports, anomaly detections, alerts and insights. Influence upstream data model design, drive KPI definitions and develop customized data solutions. Measure impact of features and initiatives and help improve customer experience. Apply machine learning concepts such as regression, Natural Language Processing, Decision Trees, and Gradient Boosting, to develop holistic view of customer behavior and features to identify areas of opportunity for experimentation, feature improvements and algorithm optimizations. Collaborate and influence cross functional partners to help deliver product objectives on time. Communicate results, insights and expectations to partners and senior leaders. Work independently in sophisticated and highly visible projects, identify risks and develop frameworks, regularly connect with collaborators and leadership teams to propose features, develop and implement experiments for investigating and answering business questions. 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 $137,500 - $207,100/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, Statistics, Applied Mathematics, Physics, Machine Learning, Computer Science, Engineering or related field and 2 years of experience in the job offered or related occupation.

Education and/or experience must include:

Utilize SQL, Spark, Hadoop and data mining techniques to extract and develop data models. Utilize Python, R, or Scala to process and develop statistical and machine learning models. Utilize Design of Experiments, Bayesian modeling and Statistical theory to develop and measure experiments and A/B tests. Utilize Machine Learning, Classification, Clustering, Forecasting and feature importance modeling to measure feature and product performance. Utilize Causal inferencing and Data Science to run observational studies. Utilize data interpretation and statistical acumen skills to infer insights from noisy and complex data sets and make recommendations. Leverage data visualization tools to monitor, alert and provide real-time analysis of features. Communicating statistical and technical output to non-technical business partners.N/A