We combined machine learning and epidemiological modelling to help public authorities manage critical healthcare resources, deliver targeted interventions and track disease spread within communities.
In early April, looking for ways to respond to the Covid pandemic, we turned our energies toward applying data science to address the evolving needs of administrators in mitigating the pandemic.
We used SEIR-like compartmental differential equation models to predict disease spread. The structure of these models was governed by the type of official case data made available to us by the Brihanmumbai Municipal Corporation (BMC) and the state government of Jharkhand.
The parameters of the model, including initial values for populations in some of the unobserved compartments, are fit to past data using a Mean Absolute Percent Error (MAPE) loss function. We apply machine learning principles in the parameter fitting process, using black box optimization methods, specifically Bayesian optimization methods such as hyperopt, to carry out the parameter sweep, and splitting time series population data into ‘training’, ‘validation’ and ‘test’ regimes. We developed a fast, robust, Approximate Bayesian Model Averaging (ABMA) method to estimate confidence intervals in epidemiological parameters and forecasts. In addition, we developed novel smoothing methods to attribute late reporting of cases to appropriate dates in the past.
ROLES AND RESPONSIBILITIES
An ML Engineer at Wadhwani AI will be responsible for building robust machine learning solutions to problems of societal importance; usually under the guidance of senior ML scientists, and in collaboration with dedicated software engineers. To our partners, a Wadhwani AI solution is generally a decision making tool that requires some piece of data to engage. It will be your responsibility to ensure that the information provided using that piece of data is sound. This not only requires robust learned models, but pipelines over which those models can be built, tweaked, tested, and monitored. The following subsections provide details from the perspective of solution design:
Early stage of proof of concept (PoC)
This is early to mid-stage of AI product development
Responsibilities during production deployment
We realize this list is broad and extensive. While the ideal candidate has some exposure to each of these topics, we also envision great candidates being experts at some subset. If either of those cases happens to be you, please apply.
Master’s degree or above in a STEM field. Several years of experience getting their hands dirty applying their craft.
ROLES AND RESPONSIBILITIES
As an ML Scientist at Wadhwani AI, you will be responsible for building robust machine learning solutions to problems of societal importance, usually under the guidance of senior ML scientists. You will participate in translating a problem in the social sector to a well-defined AI problem, in the development and execution of algorithms and solutions to the problem, in the successful and scaled deployment of the AI solution, and in defining appropriate metrics to evaluate the effectiveness of the deployed solution.
In order to apply machine learning for social good, you will need to understand user challenges and their context, curate and transform data, train and validate models, run simulations, and broadly derive insights from data. In doing so, you will work in cross-functional teams spanning ML modeling, engineering, product, and domain experts. You will also interface with social sector organizations as appropriate.
Associate ML scientists will have a strong academic background in a quantitative field (see below) at the Bachelor’s or Master’s level, with project experience in applied machine learning. They will possess demonstrable skills in coding, data mining and analysis, and building and implementing ML or statistical models. Where needed, they will have to learn and adapt to the requirements imposed by real-life, scaled deployments.
Candidates should have excellent communication skills and a willingness to adapt to the challenges of doing applied work for social good.