Here at Tempus we believe the greatest promise for the detection and treatment of cancer, cardiac & other diseases lies in building a deep understanding of the interaction between biological attributes and clinical treatment. With the advent of genomic sequencing, we can finally measure and process our genetic makeup. We now have more data than ever before, but providers often don't have the infrastructure or expertise required to easily extract the valuable insights that exist within this data.

We're on a mission to redefine how the combination of genomic, biosignal, clinical, and imaging data is used in a clinical setting through precision medicine. We are looking for machine learning, deep learning, and data science experts who are passionate about applying state of the art techniques to the processing and integrative analyses of vast amounts of data. The ideal candidate has significant expertise in the biomedical or clinical domain, and is eager to apply his or her skills to improve patient outcomes

What You'll Do

  • Analyze and integrate large diverse clinical, molecular, biosignal and imaging datasets to extract insights, and drive research opportunities.
  • Design and prototype novel analysis tools and algorithms for predicting patient outcome and treatment response in oncology and cardiology
  • Collaborate with product, science, engineering, and business development teams to build the most advanced data platform in precision medicine.
  • Interrogate analytical results for robustness, validity, and out of sample stability.
  • Document, summarize, and present your findings to a group of peers and stakeholders.
  • Provide technical leadership & expertise across multiple modeling projects.

Qualifications

  • MS/PhD degree in a quantitative discipline (e.g. statistical genetics, cancer genetics, machine learning, bioinformatics, statistics, computational biology, applied mathematics, physics, or similar).
  • 2+ years full time employment or postdoctoral experience building and validating predictive models on structured or unstructured data
  • Outstanding analytical and problem solving skills, with a particular focus on understanding the intricacies of molecular or multi-modal data sets.
  • Expert-level experience with supervised and unsupervised machine learning algorithms, and ensemble methods, such as: PCA, regression, deep neural networks, decision trees, gradient boosting, generalized linear models, mixed effect models, non-linear low dimensional embeddings and clustering.
  • Proficient in Python and SQL.
  • Experience with the following: Pandas, NumPy, SciPy, Scikit-learn, Jupyter Notebooks, and a machine learning framework such as TensorFlow, SageMaker, or PyTorch
  • Strong programming skills.
  • Thrive in a fast-paced environment and willing to shift priorities seamlessly.
  • Experience with communicating insights and presenting concepts to diverse audiences.
  • Team player mindset and ability to work in an interdisciplinary team.
  • Goal orientation, self motivation, and drive to make a positive impact in healthcare.

Nice to Haves

  • Experience working with genomic, biosignal, clinical, or imaging data.
  • Strong peer-reviewed publication record.
  • Kaggle.com competitions and/or kernels track record
  • Experience with: Git, matplotlib, seaborn, HTML5, CSS3, JavaScript, D3, Plot.ly, Flask, Dask, Docker, AWS.
  • Experience working in a Linux / Mac and AWS cloud environments.
  • Experience in agile environments and comfort with quick iterations.

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