Data Scientist, Immunology
Passionate about making a difference in the world of cancer genomics?
With the advent of genomic sequencing, we can finally decode and process our genetic makeup. We now have more data than ever before but providers don't have the infrastructure or expertise to make sense of said data. Here at Tempus, we believe the greatest promise for the detection and treatment of cancer lies in the deep understanding of molecular activity for disease initiation, progression, and efficacious treatment based on the discovery of unique biomarkers.
We are seeking an independent and motivated Data Scientist, Immunology to join our Computational Immunology group. The successful candidate will work in an interdisciplinary team, carry out data analysis, and apply best-in-class algorithms — or develop new algorithms — that directly address important biological and clinical questions. An immunology background is not required for this position. The successful candidate will be able to effectively present complex algorithms in a clear and concise manner that is accessible to a diverse audience, including quantitative, experimental, and clinical scientists.
What You’ll Do
- Research and development of novel machine learning algorithms for the product platform.
- Apply statistical and machine learning methods to analyze large, complex data sets.
- Communicate highly technical results and methods clearly.
- Interact cross-functionally with a wide variety of people and teams.
- PhD degree in a quantitative discipline (e.g. statistics, bioinformatics, computational biology, computer science, applied mathematics, applied physics or similar) or equivalent practical experience.
- Experience in one or more of the following topics: image processing, computer vision, machine learning and medical Image analysis.
- Experience developing, training, and evaluating deep-learning models using public DL frameworks, such as TensorFlow, Keras, and PyTorch.
- Experience developing, training, and evaluating classical machine learning models, such as linear and logistic regression, SVMs, Random Forests, and Gradient Boosting, as well as deep-learning models.
- Interest in immunology and immunotherapy.
- Experience working with Docker containers and cloud-based compute environments.
- Familiarity with neural network techniques (batch-norm, residual connections, inception modules, etc).
- Familiarity with medical imaging data.