Driving data generation and analysis for ML-based molecule design

In the Driving Data Generation and Analysis role at Inceptive, you'll primarily be tasked with the end-to-end design and quantitative analysis of biological datasets. You will collaborate with experimental scientists to design experiments and provide rapid feedback through detailed visualizations and results. Additionally, your tasks will include creating and deploying efficient data analysis tools, working within diverse data types like sequencing and microscopy, and validating candidate therapeutic RNA molecules through various testing methods.

Candidates for the Driving Data Generation and Analysis position at Inceptive should ideally have at least three years of experience applying computational methods to biological data. A strong academic background, such as a PhD in computer science or computational biology, is preferred. Proficiency in the scientific Python ecosystem and experience working with experimentalists to derive statistically sound results are crucial. Familiarity with various data types in molecular biology and a readiness to travel for team retreats further strengthen your application.

Inceptive is dedicated to the professional growth of its team members, especially in the Driving Data Generation and Analysis role. We offer a generous Learning & Development budget that encourages participation in conferences, courses, and access to platforms like EdX. Furthermore, you’ll have opportunities to collaborate with experienced professionals and gain knowledge in areas outside your expertise, fostering a continuous learning environment.

The 'antedisciplinary' approach at Inceptive refers to our vision of eliminating traditional silos in expertise. For the Driving Data Generation and Analysis role, this means you will not only deepen your knowledge in computational biology but also engage with other fields within the organization. This collaborative mindset fosters a fresh perspective on problem-solving and enables breakthroughs in molecular design and software development.

At Inceptive, the culture is built on humility, curiosity, and the pursuit of knowledge. As part of the Driving Data Generation and Analysis team, you will be encouraged to approach problems with a beginner's mind, which fosters innovation. We prioritize in-person collaboration and camaraderie through company-wide retreats and wellness initiatives, creating a supportive atmosphere that thrives on shared learning and discovery.

When answering this question, focus on specific projects where you've applied data analysis techniques in molecular biology. Highlight the types of data you've worked with, the statistical methods used, and the impact of your analysis on the outcomes of the projects.

Provide a structured response outlining how you would collaborate with experimentalists to define the objectives, the type of RNA to investigate, and the methodologies for data collection. Emphasize the importance of statistical robustness and the relevance of your preliminary data analysis.

List specific programming languages, libraries, and tools you have used, such as NumPy, pandas, or SciPy, and accompany this with concrete examples of how you've leveraged them in projects related to data analysis and molecular biology.

Choose a clear example that demonstrates your analytical skills—in terms of data collection, analysis, and outcome. Explain how your work influenced the project direction or led to a significant discovery.

Focus on a specific instance where your data analysis played a pivotal role in identifying problems in experiments. Share how your communication with experimental team members led to resolving issues and improving outcomes.

Discuss challenges such as data variability, noise, or incomplete datasets. Explain how you have tackled these issues in the past through effective methodologies or technological innovations.

Talk about techniques you employ, such as using proper controls, replicating experiments, and selecting appropriate statistical tests, to ensure that your results are reliable and valid.

Emphasize your belief in interdisciplinary teamwork and how you'll communicate findings promptly with your colleagues. Share specific examples where collaboration led to enhanced project outcomes.

Share your passion for the intersection of machine learning and molecular biology. Highlight specific experiences or historical breakthroughs that inspire your ongoing enthusiasm for this challenging field.

Provide details on tools you have developed, the programming languages and frameworks used, and the impact those tools had on your team’s workflow efficiency and data management capabilities.