External Author: Wadhwani AI

COVID-19 Decision Support Playbook

Post author By Arun
Post date May 23, 2022

This playbook by Wadhwani AI is a practical guide that summarises the work we did from a technical and public health perspective, to aid government bodies towards their pandemic response through a combination of predictive modelling and data analytics.

It is meant for data science practitioners and epidemiologists, who may utilise our methodology and codebase in their respective research areas. Moreover, public health professionals and government officials may glean from the data pipelines, modelling framework and analytics capabilities to forecast disease spread in communities.

AI Solutions for TB Elimination

Post author By Arun
Post date July 21, 2021

The NTEP (National TB Elimination Program) along with USAID aims to aid innovators and leverage emerging technologies such as artificial intelligence (AI) to accelerate tuberculosis elimination efforts. The NTEP has identified areas and articulated problems within the TB cascade of care that are AI-amenable. However, the NTEP is aware that this is a non-exhaustive list and is open to exploring new problem areas and solutions.

The purpose of this document is to motivate and invite individuals and organizations to develop AI-based solutions for TB. The conclusion section of the document describes the process for engaging with the NTEP and USAID on such solutions.

ML Engineer

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)

Setup and structure code bases that support an interactive ML experimentation process, as well as quick initial deployments
Develop and maintain toolsets and processes for ensuring the reproducibility of results
Code reviews with other technical team members at various stages of the PoC
Develop, extend, adopt a reliable, colab-like environment for ML

Late PoC

This is early to mid-stage of AI product development

Develop ETL pipelines. These can also be shared and/or owned by data engineers
Setup and maintain feature stores, databases, and data catalogs. Ensuring data veracity and lineage of on-demand pulls
Develop and support model health metrics

Post PoC

Responsibilities during production deployment

Develop and support A/B testing. Setup continuous integration and development (CI/CD) processes and pipelines for models
Develop and support continuous model monitoring
Define and publish service-level agreements (SLAs) for model serving. Such agreements include model latency, throughput, and reliability
L1/L2/L3 support for model debugging
Develop and support model serving environments
Model compression and distillation

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.

DESIRED QUALIFICATIONS

Master’s degree or above in a STEM field. Several years of experience getting their hands dirty applying their craft.

Programming

Expert level Python programmer
Hands-on experience with Python libraries
- Popular neural network libraries
- Popular data science libraries (Pandas, numpy)
Knowledge of systems-level programming. Under the hood knowledge of C or C++
Experience and knowledge of various tools that fit into the model building pipeline. There are several – you should be able to speak to the pluses and minuses of a variety of tools given some challenge within the ML development pipeline
Database concepts; SQL
Experience with cloud platforms is a plus

ML Scientist

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.

REQUIREMENTS

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.

DESIRED QUALIFICATIONS

B.Tech./B.E./B.S./M.Tech./M.E./M.S./M.Sc. or equivalent in Computer Science, Electrical Engineering, Statistics, Applied Mathematics, Physics, Economics, or a relevant quantitative field. Work experience beyond the terminal degree will determine the appropriate seniority level.
Solid software engineering skills across one or multiple languages including Python, C++, Java.
Interest in applying software engineering practices to ML projects.
Track record of project work in applied machine learning. Experience in applying AI models to concrete real-world problems is a plus.
Strong verbal and written communication skills in English.

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