Sanofi Group Applied Machine Learning Senior Scientist, Vaccines in Cambridge, Massachusetts
The Scientific Digital Innovation (SDI) group is working to develop and deploy state-of-the-art digital strategies/tools to meet Sanofi Pasteur's mission to become the disruptive market leader in vaccines. The SDI group seeks entrepreneurial and forward-thinking individuals to help drive Artificial Intelligence (AI) / Machine Learning (ML) / Deep Learning (DL) solutions to create value, improve efficiency and quality across the research and development pipeline.
S/he will be responsible for prototyping, optimizing and implementing machine learning and analytical models addressing complex biological questions.
S/he should have a track record of developing and implementing interpretable, high performance solutions and deploying models at scale.
S/he will have a high degree of proficiency in Python/R, SQL, deep learning libraries, cloud computing and rapidly prototyping solutions.
S/he is expected to have breadth of knowledge and experience in machine learning and ability to rapidly transform theory to an actionable prototype.
At minimum, undergraduate training focused on machine learning, applied math, computational sciences, etc. from a top-tier program and industrial internships will be considered; graduate applicants with less industrial experience will also be considered.
S/he will be ready to hit the ground running in a fast-paced environment that rewards personal initiative, collaboration, and scientific excellence.
Undergraduate or higher degree in computational sciences, machine learning or other technical fields with deep expertise in quantitative analysis and deployment of machine learning pipelines and solutions.
Broad expertise in ML (Deep Learning, Embeddings, Reinforcement and Transfer Learning) and algorithmic analysis of data.
High proficiency transforming algorithms/theory into practical applications delivering robust models that are high performance, interpretable and actionable.
A creative problem solver, skilled in divergent thinking and turning complex data into actionable insights and knowledge.
Ability to process multiple and heterogenous inputs to develop a clear understanding of a problem, issue or potential solution and translate them into practical business opportunities.
Ability to navigate ambiguity and interact effectively with unconventional people and ideas. Work with partners across the organization to turn data into critical information and knowledge for improved process understanding to facilitate data driven decisions making.
Excellent 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 data.
Understanding of drug/vaccine discovery and development processes is a plus, as well as familiarity with large, real world (biomedical) data sets.
Sanofi Inc. and its U.S. affiliates are Equal Opportunity and Affirmative Action employers committed to a culturally diverse workforce. All qualified applicants will receive consideration for employment without regard to race; color; creed; religion; national origin; age; ancestry; nationality; marital, domestic partnership or civil union status; sex, gender, gender identity or expression; affectional or sexual orientation; disability; veteran or military status or liability for military status; domestic violence victim status; atypical cellular or blood trait; genetic information (including the refusal to submit to genetic testing) or any other characteristic protected by law.
At Sanofi diversity and inclusion is foundational to how we operate and embedded in our Core Values. We recognize to truly tap into the richness diversity brings we must lead with inclusion and have a workplace where those differences can thrive and be leveraged to empower the lives of our colleagues, patients and customers. We respect and celebrate the diversity of our people, their backgrounds and experiences and provide equal opportunity for all.