P-4586 Translational Bioinformatics Staff Scientist

As a Translational Bioinformatics Staff Scientist at 10x Genomics, you will lead genomic data analysis efforts for single-cell and spatial genomics. Your key responsibilities include designing statistical frameworks, developing computational pipelines, and conducting studies that integrate multi-omic datasets to derive actionable insights in the clinical context.

To apply for the Translational Bioinformatics Staff Scientist position at 10x Genomics, candidates should hold a Ph.D. in Bioinformatics, Computational Biology, or a similar field. Extensive experience in NGS, statistical programming (R, Python), and handling complex genomic datasets is also necessary. A strong publication record and leadership experience will further enhance your candidacy.

The Translational Bioinformatics Staff Scientist at 10x Genomics plays a crucial role in translating complex genomic data into findings that can impact human health. By leading genomic analysis projects that connect biological insights with clinical applications, you help bridge the gap between experimental research and real-world health solutions.

In the role of Translational Bioinformatics Staff Scientist at 10x Genomics, you will lead projects focused on single-cell and spatial genomics, including the development of computational strategies to analyze multi-omic datasets. Collaborating with laboratory scientists and clinicians, you will drive research that leads to impactful discoveries in areas like cancer and immune response.

The work environment for a Translational Bioinformatics Staff Scientist at 10x Genomics is collaborative and innovative. You will be part of a passionate team focused on scientific excellence and interdisciplinary research, all while enjoying the flexibility of hybrid work arrangements that allow for on-site collaboration in Pleasanton, California.

In your response, detail how you've utilized NGS in prior projects, emphasizing specific techniques you've mastered and how they contributed to meaningful outcomes. Discuss any significant challenges you faced and how you overcame them.

Provide a thorough explanation of statistical methods and normalization strategies you've used to mitigate batch effects. Include examples from past experiences to illustrate your capability to manage datasets effectively.

Discuss your familiarity with integrating different omics data types, such as transcriptomics and proteomics. Highlight a specific project where you successfully integrated these data types and the insights you gained from it.

Talk about your leadership style, focusing on communication, mentorship, and collaboration. Provide examples of how you have nurtured a team's skills and facilitated a positive working environment to achieve project goals.

Share specifics about a complex analysis project, detailing the methods you used, the data involved, and the impact of your findings. Be sure to note how the analysis influenced subsequent research or clinical applications.

Discuss your strategies for staying informed, such as attending conferences, participating in webinars, and reading journals. Mention any significant breakthroughs you've learned that could inform your work as a Translational Bioinformatics Staff Scientist.

Share how you've developed or utilized computational pipelines, referencing tools and software you’ve worked with. Highlight any improvements or efficiencies you brought to existing processes.

Articulate the critical importance of bioinformatics in translating genomic data into clinical applications. Discuss how your work as a Translational Bioinformatics Staff Scientist can enhance patient outcomes and inform treatment decisions.

Focus on your communication and project management skills. Discuss previous experiences working with external collaborators, and how you successfully aligned goals while addressing potential challenges.

Explain your process for planning and writing research papers, emphasizing the importance of clarity and relevance. Highlight your publication record by mentioning notable papers while illustrating how they advanced the field.