Vice President, Translational Data Science/Computational Biology

The Vice President of Translational Data Science at Recursion leads the strategy and execution of integrating data from multiple sources to identify diseases and patient populations while leveraging advanced analytics to push forward drug discovery initiatives.

Candidates for the Vice President, Translational Data Science role at Recursion should have over 7 years of experience in computational biology, particularly in drug discovery and development, along with a proven track record of employing machine learning in significant genetics projects.

At Recursion, you can expect a dynamic and collaborative work culture where innovation is valued. The team is committed to learning from various perspectives and emphasizes delivering impactful results while upholding integrity as a core value.

The Vice President of Translational Data Science plays a crucial role in improving patient outcomes by translating complex genetic and clinical data into actionable insights that enhance precision medicine and the drug development process.

In this key role, the Vice President of Translational Data Science will work closely with leaders across various disciplines, including discovery biology, computational chemistry, and data science, to ensure integrated strategies that advance therapeutic developments.

Advanced analytics are pivotal in the Vice President of Translational Data Science role at Recursion; they enable the integration of population-level data and genome insights to identify pathways for drug development and patient personalization.

Recursion offers the Vice President of Translational Data Science opportunities for professional growth through mentorship, leadership in significant projects, and collaboration with a diverse range of experts committed to pioneering drug discovery.

When asked this question, focus on your systematic methodology. Discuss your experience in aligning machine learning models with genomic datasets, emphasizing your understanding of various algorithms and data types. Highlight specific projects where you achieved success by enhancing predictive accuracy in target identification or patient stratification.

Use this opportunity to showcase your knowledge of multi-omics data and its impact on precision medicine. Provide examples from your past roles where you developed biomarkers or genomics strategies that improved patient outcomes, ensuring to detail the methodologies applied and the results achieved.

Answer by outlining the approaches you've employed for effective patient stratification, such as developing clear inclusion/exclusion criteria based on genomic insights. Discuss your experience in analyzing patient datasets and how it influenced trial design to enhance outcomes.

Discuss your communication strategies as a leader, such as fostering an open-minded environment that encourages sharing ideas. Provide examples where you bridged gaps between departments, promoting collective problem-solving to drive project success.

Describe your validation processes, focusing on statistical methods and follow-up experiments. Mention specific tools and techniques you've used to ensure the findings are robust and actionable for advancing drug development.

Mention your continuous learning practices, such as attending conferences, participating in online courses, or engaging with academic publications. Emphasize your commitment to staying at the forefront of technology trends that impact drug discovery.

Focus on a particular project, detailing your role and contributions. Highlight the team dynamics, objectives, challenges faced, and the significant outcomes that resulted from the collaboration, underscoring your leadership skills.

Discuss your approach to evaluating computational tools by considering the tools' effectiveness, scalability, and integration with current workflows. Mention any specific tools you've implemented, and the impact they had on work efficiency and outcomes.

Express your insights on future trends, such as advancements in artificial intelligence or enhanced data integration techniques. Discuss how these trends might influence drug discovery and development strategies in the coming years.

Address this question by sharing experiences where you navigated regulatory compliance in clinical trials. Emphasize your understanding of regulatory frameworks and how you ensured that your strategies were aligned with industry standards.