Data Scientist / Statistician / Economist
Santa Clara Valley (Cupertino), California, United States
Software and Services
The individual in this role will be responsible for interpreting quantitative data and developing statistical models to forecast and monitor infrastructure demand for Apple Cloud Services.
- 2+ years of experience doing complex forecasting and data analysis
- Strong statistical background and experience with time series modeling (e.g. ARIMA, exponential smoothing, time series regression methods etc.)
- Experienced R programmer also proficient in other languages important to the ETL data pipeline (e.g. SQL)
- Experience with data visualization packages (e.g. ggplot2, plotly
- Excellent collaborator with strong written and verbal communication skills
- Comfortable working in a loosely structured organization and advancing multiple projects at once on a tight schedule
- Ability to share results with a non technical audience
- Experience building and maintaining R packages
- Innate curiosity
- Experience with bayesian time series modeling and Stan (or Stan interfaces e.g. brms, rstanarm, rstan) is a plus but not required
- Experience building interactive dashboards and apps (e.g. Shiny) is a plus but not required
- A passion for statistics, forecasting, and R is also a plus but also not required
As members of the Services Forecast & Efficiency data science team, we work with various engineering teams to understand current and future infrastructure demand (storage, network, CPU, etc.). We need to be persistent and flexible in extracting data from various sources, cleaning and curating these data, and then clearly and concisely summarizing any insights. In this role you will build models to forecast the financial impact of new hardware and software releases across different scenarios and develop internal visualization and modeling tools to facilitate data-driven decisions. You should be comfortable communicating results and other analytical findings to business partners.
Education & Experience
Minimum Bachelor’s degree in Statistics, Mathematics, Economics, or other quantitative disciplines. Masters or PhD preferred.