PopVax is an Indian full-stack biotechnology company developing novel mRNA vaccines and therapeutics using computational protein design. Our first vaccine is a next-generation COVID-19 booster which will enter a first-in-human Phase I clinical trial in the U.S. early next year – this vaccine is intended to broaden protection against both current and predicted future SARS-CoV-2 variants, reducing the possibility of a new mutation in the virus suddenly causing another massive wave of infection. We have a preclinical pipeline of 6 additional novel vaccine candidates built on our mRNA-LNP platform that we will be taking into clinical trials over the next few years. Our work to date has been funded primarily via project agreements totalling 10+ million USD with the Bill & Melinda Gates Foundation and Vitalik Buterin's public health & biosecurity organization Balvi.

We are seeking an Applied Statistician to join our team in Hyderabad (note: in-person only, we do not offer remote work) to play a key role across our programs. Responsibilities will include:

• Collating and processing diverse, unstructured biological and chemical data produced by PopVax’s scientific teams on a daily basis

• Performing basic statistical analyses such as t-tests, ANOVA, Pearson and Spearman correlations, and generating brief markdown reports 

• Producing data visualisations to fit specific requests 

• Liaising with PopVax’s scientific teams to improve and optimize data reporting methods and formats

The ideal candidate will have:

• Thorough theoretical understanding of Probability and Statistics

• A degree in Statistics, Data Science, Applied Mathematics or a similar quantitative field

• Excellent proficiency with R or Python

• Familiarity using R packages such as dplyr, ggplot2 and shiny or Python packages such as pandas, scipy, matplotlib, seaborn and plotly

• At least 2 years of experience with data processing and visualisation

• A demonstrated ability to produce clean data reports and visualisations especially using markdown-based tools like Rmd and Quarto

• A willingness to handle relatively large volumes of unstructured and poorly annotated data, working quickly on often-tight deadlines

• Basic understanding of machine learning techniques such as regression, decision trees, clustering and dimensionality reduction

We offer very competitive compensation, comprehensive health insurance covering immediate family (including pre-existing conditions), and, most importantly, a collaborative work environment focused on solving the cutting-edge multidisciplinary challenges of our novel mRNA platform.