Data Scientist for Scientific Discovery
2 days ago
At Valence Labs, we're revolutionizing the way scientific discovery is industrialized to improve lives.
About UsWe're a team of passionate researchers dedicated to developing highly-autonomous systems that spearhead a fundamental shift in treatment discovery and development for complex diseases. Our research is driven by optimism, purpose, and a shared vision for a healthier tomorrow. We publish in top journals and conferences, are committed to open-science and open-source, and maintain active research communities.
The RoleWe're seeking an experienced Data Scientist to lead the development of software and AI systems that support our mission. As a key member of our team, you'll design, implement, and deploy distributed machine learning systems at scale, utilizing state-of-the-art algorithms and exceptional problem-solving skills.
- You will collaborate with our research team on novel ML methods to accelerate drug discovery, working across units to provide both research insights and engineering solutions.
- Handle data wrangling, filtering, and preprocessing for various data types, including omics, sequential, imaging, molecular, and simulation data.
- Collaborate with an interdisciplinary team of scientists to inform and improve our models and systems.
- Engage with and contribute to open-source libraries developed by Valence and the research community.
We offer a competitive salary of $140,000 - $170,000 per year, depending on experience, along with a range of benefits, including health insurance, retirement plan, and professional development opportunities. Our team is located in London and Montreal, where you'll have access to a dynamic and inclusive work environment.
Requirements- PhD or Master's degree with industry experience.
- Proven track record in machine learning, including designing new architectures, hands-on experimentation, analysis, visualization, and model deployment.
- Strong programming skills and understanding of modern software development practices, especially in Python and PyTorch.
- Experience in building and deploying high-performance implementations of deep learning algorithms.
- Familiarity with some of the types of data used at Recursion: phenomic imaging, DNA sequencing, transcriptomics, molecular graphs, protein structures, biological databases, and quantum mechanics simulations.
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