Bioinformatics Scientist (Redwood City)

As a Bioinformatics Scientist at Tempus, your main responsibilities include developing novel assays and algorithms to detect oncology-related biomarkers using cell-free NGS data. You will conduct deep analyses of genomic datasets and execute experiments to validate your findings, which will support various regulatory submissions. Additionally, you're expected to produce high-quality documentation and communicate your results effectively with other scientific teams.

To qualify for the Bioinformatics Scientist position at Tempus, you should have a PhD or a Master’s degree in Bioinformatics, Computational Biology, or a related field, along with at least 3 years of industry experience. Proficiency in computational programming using Python or R and experience with NGS data pipelines is also required. A strong scientific reputation demonstrated through publications or successful product contributions will further enhance your candidacy.

The Bioinformatics Scientist role at Tempus is pivotal in advancing precision medicine by developing algorithms and assays that enhance our understanding of patient-specific oncological insights. By analyzing large datasets and providing actionable insights, you play an integral part in guiding treatment decisions, ensuring that patients receive the most effective therapies tailored to their individual needs.

The expected salary range for the Bioinformatics Scientist position at Tempus, if performed from California, is between $100,000 and $150,000 USD, depending on qualifications and experience. Tempus also offers a comprehensive benefits package that may include medical benefits, incentive compensation, and restricted stock units.

Key skills for a successful Bioinformatics Scientist at Tempus include a solid understanding of cancer biology and genomics, along with computational skills in programming languages such as Python and R. Experience in developing statistical models and familiarity with high-performance computing environments are also critical. Effective communication and collaboration abilities are essential as you will be working closely with interdisciplinary teams.

In responding to this question, highlight specific projects where you analyzed NGS data, detailing the tools and methods used. Discuss how your analysis led to key insights or contributed to product development, showcasing your ability to translate technical data into actionable outcomes.

Talk about specific algorithms you have created, explaining the rationale behind them and the technologies you employed. Share the impact these algorithms had on your projects and how they improved diagnostic accuracy or treatment selection.

Emphasize your attention to detail and quality control processes you implement, including validation steps or benchmarks. Explain how you document your analyses and results to maintain transparency and reproducibility.

Share a specific project that posed challenges, focusing on how you approached problem-solving and leveraged your skills. Discuss the outcome and what you learned from the experience, highlighting your resilience and adaptability.

Mention the resources you use, such as journals, conferences, and online courses. Show your commitment to continuous learning and how you apply new knowledge in your work to improve methodologies or project outcomes.

Discuss the importance of collaboration in your projects, how you communicate findings with cross-functional teams, and share examples of successful teamwork that led to significant project milestones.

Provide an overview of your methodology for statistical model development, detailing the steps you take from hypothesis formation through to model validation, including any software or programming languages used in the process.

Share your approach to task prioritization, including the use of project management tools or methodologies. Highlight how you balance competing deadlines while ensuring accuracy and thoroughness in your analyses.

Articulate your perspective on the role of bioinformatics in improving patient outcomes, emphasizing how data-driven insights facilitate personalized treatment options and lead to better care strategies.

Provide examples of how you have simplified complex data presentations for audiences without a scientific background, demonstrating your communication skills and understanding of the importance of making scientific findings accessible.