Leverage advanced analytics to enable data-driven decisions that impact how Apple employees do their best work by uncovering insights from support data, ticketing systems, app usage, and operational processes, helping to optimize the IT ecosystem and create a more seamless and productive environment.
Requirements
- 4+ years of hands-on experience with Python, SQL, and Tableau.
- 1+ years experience applying Gen AI and LLMs to real-world data analytics problems.
- Proficiency in version control and collaborative documentation practices using tools like GitHub.
- Deep understanding of statistical modeling and causal inference, including experimental and observational analysis, hypothesis testing, and measurement design.
- Strong machine learning skills, including regression, classification, clustering, time-series forecasting, NLP, and unsupervised learning.
- Advanced data wrangling and preparation skills, with experience extracting, cleaning, joining, and validating data from various sources to develop analysis-ready datasets.
- Experience building reproducible pipelines with version-controlled analyses, well-documented methodologies, and reusable workflows.
Responsibilities
- Apply your expertise in data wrangling and preparation to extract, clean, transform, and validate data from multiple systems, creating reliable datasets for analysis, modeling, and visualization.
- Use causal inference techniques on observational data to mitigate confounding and bias, generating robust insights that support sound decision-making.
- Develop models and forecasts to predict ticket volumes, staffing needs, and performance trends, enabling proactive IT support resource planning.
- Integrate Gen AI tools, such as large language models, to summarize support patterns, classify tickets, and model sentiment, enhancing insight generation and responsiveness.
- Maintain well-documented codebases in GitHub, deliver reproducible analyses, and mentor colleagues in standard processes.
- Communicate your findings clearly to technical and non-technical audiences, improving impact, strengthening data literacy, and fostering a data-driven culture.
Other
- Masters degree in a quantitative field (e.g., data science, statistics, applied mathematics, operations research, economics, the natural sciences) or equivalent work experience.
- 5+ years of experience as a data scientist, data analyst, or machine learning engineer.
- Excellent written and verbal communication skills, with the ability to present complex information clearly to technical and non-technical audiences.
- A strong dedication to documentation, ensuring collaboration and reproducibility.
- Experience with IT support analytics, including working with ticketing data, support journeys, and multi-channel interactions (Slack, email, phone, chat).
