Staff/Senior Data Scientist, Genomics Algos

As a Staff/Senior Data Scientist at Tempus, your key responsibilities include analyzing large multimodal datasets to develop AI-enabled clinical reports, creating novel algorithms for predicting cancer subtypes and treatment responses, and collaborating with product, engineering, and business development teams. You'll also focus on documenting and summarizing analytical results to share insights with peers and stakeholders.

To qualify for the Staff/Senior Data Scientist position at Tempus, you need an MS or PhD in a quantitative discipline such as bioinformatics, machine learning, or statistics. Additionally, you should have experience with genomic data analysis, strong programming skills in Python as well as proficiency in SQL databases, and a comprehensive understanding of both supervised and unsupervised machine learning algorithms.

Experience in clinical data is crucial for the Senior Data Scientist role at Tempus because the position requires working with real-world evidence and clinical trial data. This background ensures that you are equipped to handle complex datasets for developing algorithms that can potentially improve patient care and outcomes.

For the Staff/Senior Data Scientist role at Tempus, strong programming skills in Python are essential, especially with libraries like pandas, scikit-learn, and lifelines. Familiarity with SQL and cloud computing tools is also highly beneficial, as well as experience with engineering best practices for research computing, such as using Docker and version control with Git.

A Staff/Senior Data Scientist at Tempus can expect a dynamic and fast-paced environment that values innovation and collaboration. The culture emphasizes teamwork across various disciplines and encourages flexibility in priorities, allowing you to thrive and make significant contributions to the healthcare domain.

In responding to this question, emphasize your hands-on experience analyzing genomic datasets, the types of projects you've worked on, and specific techniques you applied. Share any notable outcomes, such as improved predictive models or insights that had a tangible impact on patient care.

Prepare to discuss various algorithms you have implemented, such as regression, classification, survival modeling, Kaplan-Meier, and Cox regression. Offer examples of how you adapted these methods to address specific challenges in genomic data analysis.

Be ready to explain your systematic approach to validating results, including using statistical tests, cross-validation, and assessing generalization through various model evaluation metrics. Highlight the importance of replicability and peer review in your analysis process.

When answering, choose a specific challenge related to data quality or model performance that you successfully resolved. Detail the steps you took, the collaborators involved, and the ultimate impact of your solution on the project goals.

Mention your strategies for continuous learning, such as participating in industry conferences, reading relevant journals, joining professional organizations, and networking with peers. Emphasize how you apply new findings to your work at Tempus to enhance your algorithms and analysis.

Discuss how clinical data serves as a foundation for identifying patterns, validating models, and personalizing patient care. Highlight your experience in utilizing clinical data to inform model development, improve accuracy, and drive healthcare outcomes.

Illustrate your ability to work within diverse teams by sharing examples of past collaborations with clinicians, software engineers, and business stakeholders. Emphasize your communication skills and how you foster productive relationships to achieve shared objectives.

When discussing documentation and presentations, outline your process for summarizing data analyses, creating clear visualizations, and communicating insights effectively. Mention your experience tailoring presentations to various audiences to ensure comprehension and engagement.

Discuss your proficiency in Python and any libraries like pandas and scikit-learn that you've effectively used. Mention how you incorporate tools like Jupyter for analysis and visualization, and detail any experience with SQL databases to manipulate and query data efficiently.

Articulate your passion for merging technology and healthcare, your admiration for Tempus's mission, and how the company's values align with your career goals. Mention specific aspects of Tempus's work that resonate with you, enhancing your motivation to contribute to their success.