Machine Learning Senior Scientist, Vaccines - réf. x31369123
Position Overview:Sanofi Pasteur, Vaccine R&D is strategically developing and deploying state-of-the-art digital strategies/tools to meet Sanofi Pasteur's mission to become the disruptive market leader in vaccines. Vaccine R&D seeks entrepreneurial and forward-thinking individuals to drive the development of Artificial Intelligence (AI) / Machine Learning (ML) / Deep Learning (DL) solutions to improve efficiency and effectiveness across research and development.S/he will be responsible for applying their deep expertise in the mathematics and theoretical underpinnings of machine learning to propose solutions to diverse scientific challenges in vaccinology.
These models will range over a large spectrum of learning problems, such as overcoming covariate shift in biological data and active acquisition of data (including under delayed rewards in reinforcement learning settings). The models will be applied across our vaccine pipeline, from early-stage pre-clinical research through to clinical trials, to provide insight and generate testable hypotheses.In all cases the goal is to build robust intellectual frameworks with practical and immediate impact.S/he has an advanced mastery of numerical methods in both theory and practice, backed-up by hands-on experience covering advanced algorithms, modeling and simulation, as well as essential scientific programming skills (e.g., Python/R). S/he is expected to have quality formal training in machine learning and experience in industrial settings and be ready to hit the ground running in a fast-paced environment that rewards personal initiative, collaboration, and scientific excellence.Key Responsibilities:Collaborates with stakeholders and other group experts to translate biological and vaccine development scientific challenges into rigorous quantitative goalsDevelops and evaluates computational and statistical methods for data analysis including machine learning model architecture designDevelops, deploys, and assesses the results of at-scale models at varying levels of model granularity encompassing heterogeneous preclinical and clinical dataEmploys machine learning methods such as deep learning, neural networks, reinforcement learning, and graphical models to deconvolve high dimensional experimental data and in silico modeling resultsActs as a scientific expert and partner in AI/MLMaintains a well-documented, reusable codebase, and traceable model historyMaintains awareness of latest developments in relevant fields, evaluates and applies as warrantedGenerates IP and participates in the drafting of patent filingsBasic Qualifications:Undergraduate or higher degree in computational sciences, machine learning, or other technical fields with strong expertise in mathematical modelling and applying quantitative analysesDeep expertise in causal inference and counterfactual thinking/analysis, ML (deep learning, embeddings, reinforcement and transfer learning) and algorithmic analysis of dataPractical experience proposing, prototyping and deploying novel algorithms; developing and validating new modelsAbility to process multiple and heterogenous inputs to develop a clear understanding of a problem, issue or potential solution and translate them into practical business opportunitiesExcellent analytical and problem solving as well as communication, scientific writing and presentation skills.
Ability to develop and present results as visually appealing and understandable stories based on the dataUnderstanding of drug/vaccine discovery and development processes is a plus, as well as familiarity with RNA biology and RNA-centric techniques/methods