Can you describe a time when you successfully solved a complex problem in your research?

Sample answer to the question

Sure, there was this one time during my Master's program when I was dealing with a tricky problem. We were trying to understand the correlation between two biological markers and their impact on cell metabolism. The datasets were complicated, and it was really hard to make sense of them at first. But I dug in deep, used some statistical software, and after many trial and error attempts, managed to find a significant pattern. This helped push our research forward and gave us some exciting insights into cell behavior. It was a proud moment for me and my team when we figured it out.

A more solid answer

While working on my thesis during my Master's program, I encountered a challenging problem. My project was focused on exploring the association between two genetic mutations and neurodegenerative diseases. The data was vast and entangled, consisting of genetic sequences from over 300 patients. At first, analyzing this dataset seemed almost impossible. I started by organizing the data meticulously, using advanced software like Python and R for statistical analysis. By collaboratively working with a multi-disciplinary team, including geneticists and statisticians, we were able to identify a novel link between the mutations and the onset of the disease. This finding was crucial as it not only provided a new perspective on disease mechanisms but also contributed to the field's existing body of knowledge. My ability to think critically, handle intricate datasets, while maintaining attention to detail, played a key role in overcoming this complex problem.

Why this is a more solid answer:

The solid answer dives deeper into the problem-solving experience by laying out more specific details like the nature of the research (genetic mutations and neurodegenerative diseases), the complexity and scale of the data (genetic sequences from hundreds of patients), and the analytical tools used (Python and R). The candidate also details their approach to organizing the data and the collaboration with different specialists, which indicates their ability to work in a team. This answer also implies the significance of the findings in the scientific community, aligns with the job description requirement for analytical skills, and subtly reflects the candidate's enthusiasm for learning through the use of data analysis software. However, there is room for improvement by emphasizing the contribution to team efforts, detailing problem-solving methodologies, and highlighting communication skills.

An exceptional answer

In the final year of my Master's, I spearheaded a complex project to uncover the relationship between environmental stressors and the immune response in aquatic organisms. Faced with an intricate dataset that included thousands of gene expression profiles, I leveraged my proficiency in R and Python to develop custom scripts for data visualization and pattern recognition. More challenging was the interdisciplinary aspect; I coordinated with immunologists, molecular biologists, and bioinformaticians to ensure a comprehensive analytical approach. Together, we discovered a set of biomarkers correlating to stress response that hadn't been previously identified. I meticulously documented our methods and findings, which not only advanced our understanding of immunological pathways but also led to co-authoring a published paper in a reputable journal. This experience honed my organizational skills, and the successful execution, despite tight deadlines, demonstrated my ability to multitask under pressure. Being a key player in this collaboration improved my scientific aptitude and problem-solving acumen, aligning perfectly with the role of Junior Research Scientist.

Why this is an exceptional answer:

The exceptional answer provides a comprehensive story and clearly articulates the candidate's lead role in solving a complex problem. It addresses the evaluation areas effectively, detailing the candidate's technical expertise with data analysis software, the critical thinking process involved in recognizing patterns, and the organizational skills employed to manage large datasets. The answer highlights the collaborative teamwork with different experts, which shows the candidate's capability to work in a multi-disciplinary environment. The successful outcome, publication, and relevance of the findings to the scientific community strengthen the candidate's credibility. Moreover, it reflects on how the candidate overcame pressure and tight deadlines, thus meeting the job description's requirement for multitasking and delivering under demanding conditions. It notes the candidate's focus on documentation and communication, important for the mentioned responsibilities of maintaining research records and preparing findings for publication.

How to prepare for this question

Reflect on specific instances from past projects or research work where you faced and resolved complex problems. Focus on instances that demonstrate your scientific aptitude and understanding of research methodologies, which are essential for the Research Scientist role.
Prepare to discuss the tools and statistical packages (like R, Python, or SPSS) you've used to analyze complex datasets. Be ready to explain why you chose certain tools and how they helped you achieve your research objectives.
Work on describing your organizational skills in detail, especially how you manage data, maintain records, and ensure accuracy while dealing with large amounts of information. This will align with the job's emphasis on organizational skills and attention to detail.
Consider the teamwork and collaboration aspect of the role. Think about examples where you worked with others, particularly those from different disciplines, to resolve a problem. This will show you're a team player and can contribute to a multidisciplinary team environment.
Prepare to articulate the impact of your research findings on your field of study, and if applicable, any publications or presentations that came from it. This showcases your contribution to scientific advancements and your communication skills, which are important for this role.

What interviewers are evaluating

Strong scientific and technical aptitude
Critical thinking and ability to handle complex datasets
Attention to detail
Ability to work collaboratively in a multidisciplinary team environment.
Proficiency in data analysis software and tools relevant to the field of research