*For this role we are looking for someone in the Americas*
Recast is hiring for an applied statistical modeler to join our team. This role is about helping our customers build good media mix models using the Recast platform. This is an applied role which is to say that we’re looking for someone excited about deeply understanding different businesses and using state of the art statistical modeling tools to help those businesses make better decisions. Less theory, more impact.
We think this role will be a good fit for individuals with experience doing Bayesian statistical modeling in the applied sciences at the graduate level. For example, maybe you work in a research lab doing statistical modeling of biological processes and you are considering a move into industry. This is a role where you’ll get to spend all of your time on fascinating modeling problems and there are zero publication requirements.
Here’s what you’ll do:
- Deeply learn about the Recast modeling platform and how to operate it
- Work with Recast customers to understand their most challenging modeling problems and solve them within the Recast platform
- Configure and fit our model to customer data following (and improving!) our robust Bayesian modeling workflow
- Diagnose and solve practical Bayesian modeling issues such as posterior degeneracies, multimodality, and poor sampling efficiency
- Collaborate with our research statisticians to convert practical problems into novel solutions
- Collaborate with our marketing science team to make sure they understand the implications of relevant modeling decisions on the interpretation of results
Here are the skills you’ll need to be successful:
- Familiarity with R
- A passion for causal inference
- Knowledge of Bayesian statistical modeling and related best practices
- Experience handling the output of Markov chain Monte Carlo (MCMC) algorithms (e.g., manipulating multidimensional posterior draws)
- Willingness to get a deep understanding of modern marketing and how advertising works at a fundamental, scientific level
Bonus points if:
- You love Richard McElreath’s book / course Statistical Rethinking
- You have deep experience with R as a programming language and have contributed to R packages on CRAN / Bioconductor
- You’re a strong scientific communicator
- You have used Stan for Bayesian modeling
Note: Recast is committed to building a diverse team, so if you are from an under-represented background in tech (e.g., women, non-white, etc.) please apply even if you don’t necessarily check all of the boxes here.
If this interests you, apply using the form on this page with a bit about yourself and any relevant documents/links.
