(Senior) Machine Learning Scientist, Spatial Imaging

As a (Senior) Machine Learning Scientist at Tempus, your primary responsibilities include developing and optimizing advanced machine learning models for spatial transcriptomics analytics. You'll support exploratory research and collaborate closely with diverse teams to analyze multimodal clinical datasets, ultimately driving innovations in drug development and clinical testing. Documenting and communicating your findings effectively will also be key to your success.

To be considered for the (Senior) Machine Learning Scientist position at Tempus, candidates typically should hold a PhD in computational biology, statistics, or a related quantitative field. In addition, you should possess 2+ years of experience with genomic data and machine learning approaches focused on complex diseases like cancer, as well as proficiency in Python and knowledge of machine learning frameworks like TensorFlow or PyTorch.

Successful (Senior) Machine Learning Scientists at Tempus excel in applied machine learning techniques, particularly those relevant to spatial transcriptomics and genomics data. Strong programming skills in Python are essential, alongside having experience in developing deep learning models. Excellent communication abilities and the capability to convey complex concepts clearly to diverse audiences are also crucial for success in this collaborative environment.

Tempus is committed to the continuous improvement of its employees, particularly those in roles like the (Senior) Machine Learning Scientist. By staying current on industry trends and encouraging innovation in research methodologies, Tempus fosters a culture of growth. You'll have the opportunity to apply cutting-edge research findings and best practices, ensuring that your skill set remains sharp and relevant.

The salary range for a (Senior) Machine Learning Scientist at Tempus varies by location. For those working in New York, the range is typically $140,000 to $190,000 USD. In California, it aligns closely with this range, while in Illinois, the expected salary falls between $120,000 and $170,000 USD. Actual compensation may vary based on qualifications and experience.

In answering this question, focus on specific algorithms you have implemented and the outcomes of your projects. Be prepared to discuss any challenges you faced, how you overcame them, and the impact your work had on advancing precision medicine.

An effective answer should outline your analytical strategies, including data preprocessing, feature selection, and the application of dimensionality reduction techniques. Highlight any software tools you regularly use and the methodologies you apply to extract meaningful insights from the data.

Focus on a specific example that showcases your teamwork and communication skills. Discuss the roles of various stakeholders, how you navigated differing priorities, and the strategies you implemented to ensure project success while maintaining clarity and collaboration.

Your response should emphasize your commitment to professional development, detailing how you stay updated on industry trends. Mention any journals you read, courses you take, or conferences you attend to enhance your knowledge and skills in the ever-evolving field.

Discuss your practices surrounding version control, documentation, and testing of your code. Include any frameworks or tools you utilize to facilitate reproducibility and highlight the importance of these measures in scientific research.

Share specific examples from your past work with large-scale imaging datasets, including the types of imaging techniques you've used and how you integrated those data into your analyses. Be sure to mention any challenges you faced and how you addressed them.

Discuss the strategies you utilize to distill complex data into understandable insights. Highlight the importance of using clear visuals, analogies, and a logical flow in your presentations, as well as involving your audience to gauge their understanding.

Be specific about the frameworks you have used, such as TensorFlow or PyTorch, detailing your experience with model development, training, and evaluation. Mention any significant projects where these tools were pivotal to success.

Discuss your familiarity with cloud platforms like AWS or GCP, and provide examples of projects where you leveraged these technologies for model training, data storage, or collaborations. Focus on how they improved your workflow.

Reflect on your personal motivations and how they align with the mission of improving patient outcomes in cancer care. Share any relevant experiences that have fueled your passion for this impactful work.