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

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

Elucidata Bioinformatics Scientist Job For MSc & PhD Bioinformatics, Comp Bio, Biotech. Candidates with MSc & PhD Bioinformatics, Computational Biology, Biotechnology qualifications can apply for Sr. Bioinformatics Scientist at Elucidata. Interested and eligible applicants can check out all of the details on the same below

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

Senior Bioinformatics Scientist

Location: New Delhi, Delhi, India Hybrid

About the job

Your Role:

Elucidata is seeking a bioinformatics enthusiast for the role of Senior 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 3-5 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.

Good to have:

• Understanding of Biological Problems
• Proficient in R or Python

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

1. Can you describe your experience in processing and analyzing bulk and single-cell transcriptomics data? Please provide specific examples of the tools and methods you have used. Answer: Sure, I have extensive experience in processing and analyzing bulk and single-cell transcriptomics data. For bulk transcriptomics, I have used tools like DESeq2 and edgeR for differential gene expression analysis. I have also worked with single-cell RNA sequencing data using tools like Seurat and Scanpy for data preprocessing, clustering, and visualization. I am familiar with techniques like dimensionality reduction, pseudotime analysis, and trajectory inference for single-cell analysis.
2. How have you applied statistical methods to derive insights from multi-omics biological data? Can you share an example of a project where you used such methods? Answer: In my previous project, I worked with multi-omics data integrating transcriptomics and metabolomics data. I applied statistical methods like partial least squares-discriminant analysis (PLS-DA) to identify metabolite features associated with gene expression patterns. I also used pathway enrichment analysis to gain insights into the biological pathways affected by the changes in omics data. These analyses helped in identifying potential biomarkers and understanding the underlying biological mechanisms.
3. Tell us about your experience in developing scalable bioinformatics pipelines for multi-omics data analysis. What technologies and tools have you used? Answer: I have developed scalable bioinformatics pipelines using tools like Snakemake and Nextflow, which provide workflow management systems for automating and parallelizing the analysis of multi-omics data. These pipelines integrated various steps such as data preprocessing, quality control, alignment, quantification, and downstream analysis. I have also utilized cloud computing platforms like AWS and Google Cloud to enable scalable and efficient processing of large-scale multi-omics datasets.
4. How do you approach innovation in implementing new tools and pipelines for multi-omics data analysis? Can you give an example of a project where you implemented a novel approach? Answer: When it comes to innovation in implementing new tools and pipelines, I believe in staying updated with the latest research and developments in the field. I actively explore scientific literature and attend conferences to identify novel methods and approaches. One project where I implemented a novel approach was in the integration of proteomics and transcriptomics data. I devised a method to integrate these two omics layers using network-based analysis and identified key regulatory modules connecting the two datasets, providing a deeper understanding of the biological system under investigation.
5. Have you led any teams or projects in your previous roles? How did you ensure successful collaboration and independent work within the team? Answer: Yes, I have led teams and projects in my previous roles. To ensure successful collaboration, I emphasized effective communication and regular meetings to align on project goals and progress. I encouraged open discussions and brainstorming sessions to foster creativity and collaboration within the team. Additionally, I delegated tasks based on individual strengths and expertise, allowing team members to contribute independently while providing necessary guidance and support. Overall, I promoted a collaborative and supportive work environment to achieve project success.

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