Data Scientist II

Summary

Join Valo Health's growing in vitro modelling team as a data scientist, contributing to data science, tissue engineering, and drug discovery. You will focus on advancing the engineering of human tissue models, particularly cardiac and skeletal muscle models. As part of a team developing computational models, engineered tissue assays, statistical models, and machine learning tools, you will play a key role in optimizing in vitro analysis platforms and experimentation. This role involves collaborating with scientists, engineers, and domain experts across traditional industry boundaries to build powerful computational tools for drug discovery and development.

Requirements

• 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

Responsibilities

• Collaborate with biologists and computational scientists to visualize, preprocess, and analyze datasets generated from human tissue models and bioengineering experiments
• 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