Project Description
Data Scientist / Statistician / Economist
Santa Clara Valley (Cupertino), California, United States
Summary
- Strong background in statistics or econometrics: regression analysis, causal inference, time series analysis, GLM, logistic regression, probability theory, regularization, interest in machine learning algorithms
- Work with various engineering teams to understand current and future infrastructure demand (storage, network, CPU, etc.)
- Build models to forecast the financial impact of new hardware and software releases across different scenarios
- Develop internal visualization and modeling tools to facilitate data-driven decisions
- Present results and other analytical findings to business partners
- 2+ years of experience in time series analysis, 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) and advancing multiple projects at once on a tight schedule
- Excellent collaborator with strong written and verbal communication skills
- Ability to share results with a non-technical audience
- Innate curiosity
- Experience in modeling (e.g. bayesian structural time series, dynamic linear models)
- Experience with Stan, Stan interfaces (e.g. brms, rstanarm)
- Experience building and maintaining R packages
- Advocate and practitioner of version control and reproducible code
Description
Work with various engineering teams to understand current and future infrastructure demand (storage, network, CPU, etc.) Build models to forecast the financial impact of new hardware and software releases across different scenarios Develop internal visualization and modeling tools to facilitate data-driven decisionsPresent 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