Data Scientist

APPLE INC has the following available in Austin, Texas. Serve as a Data Scientist on the Employee Productivity & Support team. Act as the technical lead for data science projects focused on improving internal IT support systems and employee productivity. Drive project execution from scoping to delivery and mentoring junior data scientists on advanced analytical methodologies and coding standards. Apply expertise in data wrangling to design and build data pipelines. Extract, clean, transform, and validate data from disparate company systems, including IT ticketing platforms, application logs, and multi-channel communication data (Slack, email, phone, chat), to create reliable, analysis-ready datasets. Develop, validate, and deploy production-level machine learning models, including regression, classification, clustering, time-series forecasting, and Natural Language Processing (NLP), to identify operational inefficiencies and drivers of employee satisfaction. Develop and implement statistical and time-series forecasting models to predict key business metrics, including IT support ticket volumes, performance trends, and staffing requirements, to enable proactive resource planning. Apply causal inference techniques to observational data to mitigate confounding and bias. Design, execute, and analyze controlled experiments (e.g., A/B tests) and other hypothesis tests to measure the impact of new tools and process changes on employee productivity. Integrate and apply Generative AI and Large Language Models (LLMs) to perform complex analytical tasks, including automated ticket classification, summarization of unstructured employee feedback from support interactions, and sentiment analysis. Utilize Git for version control to maintain well-documented, reproducible codebases in a collaborative environment. Perform code reviews and deliver reproducible analyses and reusable workflows based on established methodologies. Communicate complex analytical findings, statistical results, and model outputs to technical and non-technical audiences, including director-level leadership, through formal presentations, written reports, and the creation of business intelligence dashboards in Tableau. Create and maintain comprehensive technical documentation for data sources, analytical models, and codebases, including data dictionaries, model cards, and project summaries, to ensure collaboration, transparency, and reproducibility of results. 40 hours/week.