Data Scientist II, Tissue Engineering

Join a growing in vitro modelling team as a data scientist supporting our efforts in data science, tissue engineering, and drug discovery. As a data scientist you will use your skills and experience to advance the engineering of human tissue models, focusing on cardiac and skeletal muscle models.

You will be part of a team responsible for developing and building computational models, engineered tissue assays, statistical models, and machine learning based tools for use throughout our drug discovery pipeline. Focus will be on optimizing our in vitro analysis platforms and experimentation, utilizing methods including multivariate time-series analyses, computer vision, and deep learning based approaches.

Successful candidates will work with a diverse set of scientists, engineers, and domain experts in ways that cut across traditional industry boundaries, building powerful computational tools to advance the discovery and development of new medicines.

What You'll Do...

• Collaborate with biologists and computational scientists to visualize, preprocess, and analyze datasets generated from human tissue models and bioengineering experiments.
• Perform multivariate time-series analyses to integrate datasets from multiple sources to better understand complex relationships and dynamic interactions between engineered tissues and novel compounds
•  Design and implement computer vision based tools for developing bespoke biological assays
• Create clear and informative data visualizations and statistical summaries to communicate findings to technical and non-technical colleagues, including collaboratively designing and building out user interfaces.
• Development of predictive models and algorithms to identify factors impacting the development, manufacturing, and use of tissue models.
• Work with scientists and engineers to optimize experimental conditions and model in-vitro phenotypic responses to chemical and genetic perturbations to better understand complex biological systems in pursuit of optimal drug candidates.
• Build models utilizing large & diverse datasets to explain experimental variability and inform scientists how to optimize experiments and engineered tissue production using multivariate analyses, time series data processing, and other statistical and machine learning modelling.

What You Bring...

• A Ph.D. + 1-2 years of experience or Master’s degree + 3-4 years of experience, or comparable experience in a relevant computational science field (e.g., computational biology, bioengineering, biostatistics).
• Strong background in data processing and analysis.
• Familiarity with statistics as well as machine learning techniques and tools, such as neural networks, generative models, ensemble learning, regression, classification, and regularization.
• Proficient in Python and experience using data analysis libraries, such as Pandas, NumPy, SciPy, Scikit-learn, and TensorFlow/PyTorch.
• Familiarity with version control for code, such as Gitlab or Github.
• Familiarity with bioengineering principles and human tissue models , and with how computational biology can be applied to support their development and use for drug discovery.
• Strong communication skills for effective collaboration and results presentation.