Senior Data Scientist

The Senior Data Scientist at Tempus is responsible for leading independent analyses in real-world evidence studies with pharmaceutical partners, deriving real-world endpoints, interpreting analysis results, and communicating findings effectively. Additionally, you'll collaborate with teams to enhance data quality and stay updated on oncology guidelines and methodologies.

To qualify for the Senior Data Scientist position at Tempus, candidates should possess an education in epidemiology, biostatistics, public health, or a related field, along with substantial experience in data science or real-world data analytics. A PhD with 1+ years or a Master's degree with 3+ years of experience is preferred. Proficiency in R and SQL is also essential.

Tempus encourages continuous learning and supports the professional growth of its Senior Data Scientists by providing opportunities to collaborate with diverse teams on innovative projects. Staying updated on advancements in the oncology field and employing best practices in data analysis are essential components of development at Tempus.

As a Senior Data Scientist at Tempus, you will utilize tools such as R and SQL for data analysis, potentially exploring cloud platforms like AWS or Google Cloud Platform. Familiarity with Git or code collaboration tools for production-level coding is also beneficial for this role.

The projects undertaken by the Senior Data Scientist at Tempus will primarily involve analyzing real-world data related to oncology studies, evaluating treatment outcomes, and providing strategic recommendations to pharmaceutical partners based on research findings.

When answering this question, emphasize specific projects where you handled observational data, detailing the methodologies used, the outcomes, and how you collaborated with clinical or research teams to derive valuable insights.

Focus on articulating the project’s context, your analytical approach, and the decision-making process you went through. Make sure to highlight the impact of your recommendations on stakeholder outcomes.

Discuss your familiarity with relevant regulations and ethical considerations in data science. Explain the steps you take to uphold compliance, such as regular training and adhering to established protocols in data handling.

Provide a robust overview of the statistical tools you’re proficient with, such as R and SQL. Give specific examples of how you've applied these tools in your analyses, showcasing your data handling capabilities.

Illustrate your commitment to professional development by discussing sources such as academic journals, webinars, and industry conferences. Highlight any specific resources that have significantly informed your work.

Talk about any relevant experiences where you worked with pharma teams, focusing on the nature of the collaboration, your role, and how you ensured effective communication and alignment of objectives.

Select a particular challenge related to data quality or interpretation and describe your problem-solving process. Stress your analytical thinking and how you utilized cross-functional teamwork.

Share a specific instance where you successfully translated complex data into understandable insights for a non-technical audience, detailing the strategies you used to facilitate understanding.

Describe your strategy for managing competing deadlines, which might include utilizing project management tools, setting clear priorities, and regular status updates to stakeholders to ensure alignment.

Express your passion for impacting patient outcomes, mentioning specific aspects of healthcare data that motivate you, such as innovation in methodologies, the drive for precision medicine, or collaborative problem-solving in clinical research.