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 Austin, Texas. Develop and implement data science techniques or analytics applications to transform raw data into meaningful information using data-oriented programming languages and visualization software. Responsible for ad hoc data pulls, building and maintaining Tableau dashboards, comprehensive driver analyses and machine learning projects. Apply data mining, data modeling, natural language processing, and machine learning to extract and analyze information from large structured and unstructured datasets. Visualize, interpret, and report data findings. Partner with other teams to help define data issues. Identify and create metrics, build data solutions using dashboard and machine learning models. Implement and socialize data science solutions. Ability to quickly switch contexts and perform meticulous analysis in a short period of time with acute attention to detail. Steward data science into larger organizations. Provide guidance on standard methodologies and cultivate maturity in consumers of data science products. 40 hours/week.

• Master's degree or foreign equivalent in Business Analytics, Data Science, Business Intelligence, Statistics, Applied Mathematics or related field and 3 years of experience in the job offered or a related occupation.
• 3 years of experience with each of the following skills is required:
• Using scripting language Python to use python to perform data wrangling, cleaning, manipulation, data modeling, exploratory data analysis, visualization, and training machine learning models
• Tableau performing exploratory data analysis and build interactive dashboards and reports by combining multiple data sources via joins, blends, and merges. Create nested LOD calculations and parameters to allow users to interact with complex visualizations like Sankey charts, radar charts, and stories
• Excel to manipulate and conduct data validation, conduct exploratory data analysis and create collaborative templates.
• Machine learning for predictive and explanatory modeling, including supervised and unsupervised techniques such as SVM, linear and logistic regression, random forest, gradient boosted machines, hierarchal clustering, k-means, k nearest neighbors, and other algorithms.
• Statistical analysis to distinguish random processes from deterministic ones. Specifically, using hypothesis tests (e.g., t-test, ANOVA), linear models, and describing different distributions (e.g., normal, Poisson, etc.) to derive insights from data.
• Use SQL to create data models for visualization and analysis. Create complex data models using chained common table expressions, window functions, and other advanced manipulations.
• Natural Language Processing (TF-IDF, word2vec, BERT) and recommendation systems (e.g., matrix factorization, cosine similarity) to manage text data and and perform tasks like text classification, entity recognition, sentiment analysis and more

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