Statistician (time series/stochastic processes) who can write a predict API in R (plumber)

We are seeking someone who is capable of modeling discrete-time stochastic processes in R (using packages like mgcv and rstan) as well as operationalizing model(s) as prediction service/API endpoints (using plumber and docker).

The task is straightforward – we would like to create an internal RESTful API service which will receive a vector (m x 1) from the caller, plus optionally some (1-2) covariates/features, and which will perform the learning task before returning an array of forward estimates and derivatives of the learned function to the caller.

If you’re strong in statistics and familiar with/understand approaches like gaussian processes for machine learning, arimax + post-hoc smooths, and in creating docker images to expose your prediction function, then please reach out or submit a proposal so we can discuss in more detail.