Location

Palo Alto, CA

Job Description

Rhombus’s PhD Statisticians will be using advanced statistical analysis to maintain and improve modeling and simulation models to drive analytically defensible decisions. They will create and apply and evaluating performance of advanced analytical and mathematical models to solve complex decision-making problems. These analytically informed decision-making augmentation frameworks enable users to more confidently select optimal courses of action.

They will use pen and paper, Python, high performance computing, cloud computing, big data analytics, and data visualization tools and techniques to improve system designs, to assist in designing models and model performance frameworks, evaluating existing models and model performance frameworks, to minimize model drift, and to achieve balanced model performance parameters based on use case specifications.

There are various opportunities within Rhombus for the right individuals to make an impact. Successful candidates will bring a systems thinking mindset, a solid foundation in statistical techniques, modeling and simulation skill sets, and the ability to communicate clearly in multiple technical contexts in a fast-paced setting. 

Required Skills/Qualifications:

• PhD Degree in Statistics or Mathematics required. (Current PhD students will be considered)
• Deep experience with modeling and simulation (multi-scale)

Preferred Skills/Qualifications

• Experience and knowledge of modern data analysis and data management techniques. We're seeking candidates who can code efficiently in Python (R is a good foundation). 
• Experience in the use of linear programming, principal component analysis, cluster analysis, univariate and multivariate regression analysis, reliability, and time-series analysis methods to form predictive models for specific response variables.
• Advanced skills in Excel, including complex formulas and Cognos Report Studio/Framework Manager, VBA, JMP, MATLAB, Minitab, R, SAS, and BI tools/frameworks.
• Experience presenting and leading with substantial exposure and involvement with data science, cloud infrastructure, solution architecture, application development, and data engineering.
• Effectively work across teams to problem solve, define scope, write technical specifications, quantify and track success metrics, and manage against a plan.
• Excellent presentation, written, communication with keen attention to detail, and interpersonal skills.