Job Description

Job Description

Job Description

With 9 offices across the country, and a dynamic team of employees we’ve created an open-minded culture of collaboration. At 9Rooftops, you’ll not only have the opportunity to work on brands big and small, but also be a critical part of an agency that believes in the power of continual improvement and that every employee should have a say in how things work.

9Rooftops is looking for our next Statistician to help our clients answer their most pressing business challenges and questions. We are a bold marketing agency built for brands who need results now. There is extensive creative freedom for building and solving the problems at hand without unnecessary constraints on modeling techniques—as long as those modeling techniques answer the question.

As a Statistician at 9Rooftops, you will use modern inferential statistical techniques (Directed Acyclic Graphs, hierarchical/multi-level regression, Bayesian statistics, state-space time series, MCMC, HMC) and experimental design (difference-in-difference, posterior matching, etc.) to help brans rise above the competition with culturally relevant high-performance creative solutions that generate energy, action and revenue.

The ideal candidate is creative, gritty, scrappy, flourishes under ambiguity, and is passionate to learn, with the ability to explore multiple challenges and models concurrently. The Statistician is passionate about Math, Statistics, Data Science, and Machine learning, while be curious and creative, exploring new statistical/data science techniques to drive innovation and solve client challenges.

Some of the questions you might be answering:

• What was the impact of our pricing changes on overall sales and profitability?
• What would happen if we shift our media spend channels?
• What is the optimal level of advertising spend by month for the next 6 months?
• How do we measure the impact in changing strategies or tactics across the country?

Skills required:

• Deep understanding of parametric statistics, causal statistical modeling, experimental design
• In-depth knowledge of advanced modeling techniques like…
  • Causal modeling, spurious correlation, confounding variables, collider bias, post-treatment effect bias, directed acyclic graphs (DAGs)
  • Hierarchical/Multi-level Regression (random, fixed, mixed effects)
  • Econometrics, state-space models, decompositions, forecasting, log-linear models, elasticity
  • Estimation techniques MCMC
  • Experimental design, measurement error, etc.
• Foundational Data Science and data understanding skills
  • Querying data bases, connecting to APIs, finding open-source data
  • Restructuring, imputing, feature engineering, “cleaning” (we all “clean” our own data and believe it’s a fundamental part of understanding the data used in the analyses)
  • Robust exploratory data analysis practices (visualization of marginal and joint distributions as well as time series)
  • Python or R or Julia skills are a must
• Secondary analytical skillsets
  • Unsupervised Machine Learning (clustering, dimension reduction/compression techniques like PCA, KPCA, T-SNE & UMAP, etc.)
  • Classification methods and scoring functions (SVM, RF, NN, etc.), especially unbalanced classes
  • Cloud environments (AWS, but experience with Azure or Google Cloud is cool with us)

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