Elucidata Bioinformatics Job For MSc & PhD Bioinformatics, Comp Bio, Biotech


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Elucidata Bioinformatics Job For MSc & PhD Bioinformatics, Comp Bio, Biotech

Hey there, if you are looking for some assistance with the possible interview questions and answers for the role of Bioinformatics Scientist II at Elucidata, then check below

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Bioinformatics Scientist II

Location: Remote Job

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Required Experience: 2 – 5 Years

About the job

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Your Role:

Elucidata is seeking a bioinformatics enthusiast for the role of Bioinformatics Scientist. In this role, you will get the opportunity to lead and manage customer-facing projects and multidisciplinary teams, apply your data science skills to solve complex biological problems, improve the understanding of biological systems and diseases and accelerate drug discovery using machine learning and AI.

Key Responsibilities:

  • Use your understanding of biological problem statements and omics data to present compelling scientific presentations and develop solutions on the product.
  • Hands on experience in processing and analysing bulk and single-cell transcriptomics data.
  • Analyse multi-omics (proteomics, metabolomics, transcriptomics etc) biological data to derive relevant insights using state-of-the-art statistical methods.
  • Innovate to implement new tools and pipelines, improve existing pipelines and algorithms for multi-omics biological data analysis and visualization.
  • This role also brings the opportunity to work with a dynamic team of data scientists, product managers and engineers to translate customer requirements into exciting results, features and products on our platform.
  • Work closely with account managers to nurture and grow accounts.

Requirements:

  • PhD or Masters (with 3-4 years of relevant experience) in Bioinformatics, Computational Biology, Biotechnology or related technical discipline.
  • Relevant experience in multi-omics data analysis, development of scalable bioinformatics pipelines and experience with public omics repositories (eg. TCGA, GEO, CCLE, DepMap etc)
  • Proficient in a programming language used for data analysis such as Python and/or R.
  • Hands-on experience applying computational algorithms and statistical methods to structured and unstructured big data.
  • Demonstrated success in collaboration, and independent work.
  • Excellent communication and presentation skills.
  • Experience of having led teams and projects is preferred.

Skills:

Research, Team leadership, Bioinformatics, Python/R programming

APPLY ONLINE

  1. Can you describe a project where you led a multidisciplinary team to apply data science skills and solve complex biological problems using bioinformatics techniques?
    • Answer: In a recent project, I led a team consisting of data scientists, biologists, and engineers to analyze bulk and single-cell transcriptomics data. We applied advanced statistical methods and machine learning algorithms to derive meaningful insights into gene expression patterns. Through effective collaboration and utilization of bioinformatics tools, we successfully identified potential biomarkers associated with a specific disease condition.
  2. How do you stay updated with the latest advancements in multi-omics data analysis and bioinformatics pipelines? Can you provide examples of tools or resources you frequently use?
    • Answer: As a bioinformatics scientist, I am committed to staying up-to-date with the latest advancements in the field. I regularly follow scientific journals, attend conferences, and participate in online communities focused on bioinformatics. I also make use of public omics repositories such as TCGA, GEO, CCLE, and DepMap to explore publicly available datasets. Additionally, I contribute to open-source projects and engage in discussions on platforms like GitHub.
  3. Describe a situation where you had to optimize or develop a new bioinformatics pipeline to handle large-scale multi-omics datasets. How did you ensure scalability and efficiency?
    • Answer: In a previous project, we encountered a challenge when analyzing massive multi-omics datasets. To address this, I optimized the existing pipeline by implementing parallel computing techniques and utilizing distributed computing resources. Additionally, I leveraged cloud-based platforms such as AWS or Google Cloud to scale our analysis infrastructure and improve computational efficiency. This approach allowed us to process large datasets in a timely manner and deliver accurate results.
  4. Can you provide an example of a time when you effectively communicated complex bioinformatics concepts and findings to a non-technical audience?
    • Answer: In a collaborative project, I was tasked with presenting bioinformatics findings to a group of stakeholders from diverse backgrounds. To effectively communicate complex concepts, I employed visual aids such as clear data visualizations and simplified explanations without compromising scientific accuracy. I also emphasized the practical implications of our findings and connected them to the broader context, ensuring that the non-technical audience could grasp the significance of the results.
  5. How do you approach team leadership and collaboration in bioinformatics projects? Can you share an experience where you successfully led a team to accomplish project goals?
    • Answer: I believe in fostering a collaborative and inclusive environment where team members can thrive. I encourage open communication, active participation, and knowledge sharing within the team. In a recent project, I led a team of bioinformaticians and biologists to develop a novel bioinformatics pipeline. Through effective delegation, regular progress updates, and facilitating brainstorming sessions, we successfully delivered the pipeline within the specified timeline while ensuring the highest quality standards. The project’s success was attributed to the cohesive teamwork and collective effort of the entire team.

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