Associate Statistics Director
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Job Description
Associate Statistics Director in Advanced Analytics working with applied statistical techniques and AI in a collaborative environment. Responsible for model development and team coaching.
Responsibilities:
- 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.
Benefits:
- Health insurance
- Retirement plans
- Paid time off
- Flexible work arrangements
- Professional development
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