Data Science Senior Consultant

Key Skills

Bayesian networks, Conjoint analysis, Data Classification, Data discretization, Discrete Choice Modeling, Factor analysis, Gabor Granger, Gradient Boosting trees, Hierarchical Clustering, Machine learning techniques, MaxDiff, Multinomial Logit, Python Programming, R Programming, Regression Analysis, SAS, Sawtooth Software, SPSS Statistics, Statistical modeling, Text segmentation, Time series, Van Westendorp

Job Description

Our Advanced Analytics and Data Science (AA&DS) at MetrixLab seeks a data science candidate with a graduate degree in Statistics, Market Research, or Social Science. The candidate will have five or more years of experience in marketing science, data science, or related field.

Responsibilities:

The candidate will have excellent writing and communication skills, and client management experience. The main responsibilities are:

• Prepare and impute missing data using SAS, SPSS, R, or Python.
• Collaborate with other members of the Data Science team in a fast paced and competitive, but collegial environment.
• Recommend survey designs for proposals and provide material for RFPs.
• Consult on questionnaire development for custom market research projects.
• Support MetrixLab’s solutions that are based in data science and apply them to practical research issues.
• Multitasking on concurrent projects via efficient time management.
• Disseminate research findings to clients in a clear non-technical style.
• Provide training on custom Excel-based simulators.

*Even if you do not meet all the qualifications, please apply, we want to hear from you!

Note: While efforts have been made to ensure the accuracy of this position description, it is not warranted to be an exhaustive recitation of all position duties; the incumbent may be required to perform duties beyond those listed above.

Required skillset will include (but not limited to):

• Data exploration including correlations and statistical significance testing.
• Key driver analysis using Regression-based models.
• Machine learning, e.g., ensemble methods and Gradient Boosting Machines.
• Segmentation, factor analysis, and classification methods (e.g., Discriminant Analysis, Multinomial Logit, and Support Vector Machines).
• Experience with Sawtooth Software, including HB/CBC, SSI Web, and MaxDiff Designer.
• Conjoint analysis, Discrete Choice Modeling, Maximum Difference Scaling, and experimental designs.
• Pricing models including Gabor Granger and Van Westendorp.
• Perceptual mapping, including correspondence mapping and multidimensional scaling.
• Proficiency in SPSS and/or SAS.
• Fluency in R or Python.

Listed below nice to have but not required skills:

• Bayesian Networks.
• Bayesian statistics, e.g., Hierarchical Bayes Modeling.
• Timeseries modeling and ad effectiveness measurement.