RESILIENCE is a first-of-its-kind manufacturing and technology company dedicated to broadening access to complex medicines and protecting biopharmaceutical supply chains against disruption. Founded in 2020, the company is building a sustainable network of high-tech, end-to-end manufacturing solutions to ensure the medicines of today and tomorrow can be made quickly, safely, and at scale. RESILIENCE will offer the highest quality and regulatory capabilities, and flexible and adaptive facilities to serve partners of all sizes. By continuously advancing the science of biopharmaceutical manufacturing and development, RESILIENCE frees partners to focus on the discoveries that improve patients’ lives.
The Data Scientist will be collaborating with others at Resilience with experimentation in new and emerging data science technologies such as Machine/Deep Learning and Artificial Intelligence, exploiting established statistical tools such as multivariate regression analysis, and utilizing data modeling and optimization tools.
Resilience is looking for a smart, motivated scientist who enjoys taking on complex challenges, work well in an agile, dynamic environment, and care about high-quality engineering best practices
- Design and implement data preprocessing, integration, and analysis solutions
- Will design and develop robust, modular, and reliable data monitoring and visualization tools in collaboration with our Research team to understand and interpret data
- Partner with our Software Engineering team to build and deploy production analysis tools
- Provides data science subject matter expertise in the planning and execution of cell and gene therapy projects that require advanced analytics methods, ensuring the maximum value is realized for data generated.
- Working with multi-disciplinary teams to ensure that models are translated into data solutions.
- Provide in-depth statistical and data analytical expertise across the organization by collaborating with Research, Development, Quality, Manufacturing, and Sciences Teams.
- Demonstrated machine learning experience
- Python fluency; strong programming practices utilizing version control systems (e.g., Git)
- Facility with scientific computing tools: NumPy, SciPy, TensorFlow/PyTorch, or equivalents
- Ability to deal well with ambiguity and work independently and as a multidisciplinary analytical research team member in a fast-paced environment.
- Have the initiative, curiosity, a bias for action, and a problem-solving attitude, and desire to bring innovative, simplified solutions to complex problems
- High personal standards of reliability and a track record of working on challenging biological problems and manipulating biological data sets (e.g., microscopy, gene expression, genomics)
- Some background knowledge in computational biology, immunology, wet-lab experimental procedures, and data analysis is a plus.
- Familiarity with AWS or similar cloud PaaS & IaaS services
- Up to 2 years experience with an MS or Ph.D., or equivalent, in a quantitative field, e.g., data science, physics, math, computer science, theoretical neuroscience
- Hands-on work experience with large complex data sets, programming languages such as R, Python, SQL, and Big Data technology, statistical modeling, and translation of results to a non-technical audience preferred.
- Experience with deep learning, Bayesian modeling, computer vision, supervised and unsupervised learning techniques
- Experience with optimization and statistical data analysis tools.
- Experience in a fast-paced startup environment preferred