Associate Statistics Director

Role Overview

• Working as part of our dynamic Advanced Analytics team using a wide range of applied statistical techniques, AI, and interactive application development approaches to analyze data, visualize results, and interpret findings for business decision support.
• Collaborate to help project teams design research that overcomes challenges and achieves clients’ business objectives
• Independently build models/quality check others’ models and associated tools for specific business needs, including design and improvement of models for branded solutions
• Evaluate new methodologies for their robustness and application; integrate into Adelphi offerings
• Go-to expert for model application development (R-Shiny, Excel, Visual Basic, Python, PowerBI, etc.)
• Client-facing member of the proposal/project team for advanced methods presentations, dialogue, application demonstrations
• Provide input into project design elements to optimize for statistical analysis
• Lead analytic brainstorming with advanced methods and project teams to solve analytic challenges and create bespoke analysis elements on the fly
• Go-to coach for advanced methods team coaching on statistics, coding, model/app development

Requirements

• Master’s or doctorate degree, or equivalent applied experience
• Minimum of 6-8 years of experience
• Broad knowledge of statistical techniques
• Experience with data manipulation, weighting, sample size calculation and significance testing
• Proficiency building war gaming/simulation models/interactive dashboards (R-Shiny, Excel, VBA, or other applications)
• Excellent ability to understand and work with numbers
• Ability to work to tight client driven deadlines
• Experience with multivariate statistics (regression, conjoint and discrete choice methods and segmentation)
• Ability to explain analyses and statistical outputs in terms non-statisticians can understand
• Proficient in modelling with categorical and ordinal data through the use of multinomial and ordinal regression methods and log-linear and logit methods
• Proficient integrating survey research with secondary data sources for modelling and data validation purposes
• Strong experience with advanced methods such as time series forecasting, Structural Equation Models (SEM), Path Analysis, and Partial Least Squares Path Models (PLS PM)
• Strong experience with hierarchical models, Bayesian approaches, Hidden Markov Models, Latent Class Models, and Monte Carlo methods
• Strong experience with Machine Learning techniques such as k-means, Gaussian Mixtures, Support Vector Machines (SVM)
• Exposure to Artificial Neural Nets and Kohonen Maps is a plus
• Experimental design
• Knowledge and advanced use of R and at least two of the following: Sawtooth CBC/HB, SPSS, Github, Matlab, Python, XLStat, SAS.

Tech Stack

• Python
• VBA
• Go

Benefits

• Health insurance
• Retirement plans
• Paid time off
• Flexible work arrangements
• Professional development