Bioinformatics Scientist II Role at Elucidata – Apply Online

Elucidata Bioinformatics Scientist Job Opening – Freshers PhD Apply Online

Elucidata Bioinformatics Scientist Job Opening – Freshers PhD Apply Online. Bioinformatics Scientist-II role at Elucidata – latest bioinformatics job – Apply now. Interested and eligible applicants can check out all of the details on the same below

Role: Bioinformatics Scientist II

Type: Full Time

Job Code: EDCPL-85777

Location: Bangalore, Karnataka, India & Delhi, India

No of position: 1 (one)

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:

• Ingest, curate and analyse multi-omics (single-cell, proteomics, metabolomics, transcriptomics etc) biological data in the public domain using APIs, pipelines and ML curation models on the Polly Platform.
• Transform and curate public and customer data for consumption on the Polly Platform and enable downstream analysis and multi-omics integration
• 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.
• Write production-ready code for bioinformatics algorithms, pipelines, visualizations and data reporting.
• Work closely with account managers to nurture and grow accounts.
• Effectively communicate and deliver curation, processing and analyses to various stake-holder
• 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.

Requirements:

• Fresh PhD or Masters (with >3 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)
• Basic knowledge of data standards and ontologies and their importance in structured data management.
• Proficient in a programming language used for data analysis such as Python or R.
• Hands-on experience applying computational algorithms and statistical methods to structured and unstructured big data.
• Demonstrated success in collaboration, and independent work.
• Team player with excellent communication and presentation skills.
• Think independently and also contribute to an active intellectual environment

Required Experience: 2 – 4 Years

Skills required: Research, Team leadership, Bioinformatics, Python/R programming

APPLY ONLINE

Here are some interview questions and sample answers for the Bioinformatics Scientist-II role at Elucidata :

1. Question: Can you explain your experience with multi-omics data analysis and how you have applied it to solve biological problems in the past? Answer: In my previous role as a Bioinformatics Scientist, I have extensively worked with multi-omics data, including single-cell, proteomics, metabolomics, and transcriptomics data. For instance, I was involved in a project where we analyzed bulk and single-cell transcriptomics data to understand the gene expression patterns in a specific disease condition. By integrating this data with proteomics and metabolomics data, we gained a comprehensive view of the underlying biological processes and identified potential biomarkers for further investigation.

2. Question: How comfortable are you with using APIs, pipelines, and machine learning models for data curation on the Polly Platform? Answer: I am highly proficient in using APIs, pipelines, and machine learning models for data curation on the Polly Platform. In my previous projects, I have utilized APIs to access and retrieve data from public omics repositories like TCGA, GEO, CCLE, and DepMap. I have also developed and optimized data pipelines to efficiently ingest, preprocess, and curate multi-omics data, making it ready for downstream analysis and integration on the Polly Platform.

3. Question: Describe a challenging bioinformatics problem you encountered and how you approached solving it using statistical methods. Answer: One challenging problem I faced involved integrating proteomics and metabolomics data from different sources with varying data structures. To address this, I applied advanced statistical techniques, such as data normalization and transformation, to make the datasets comparable. Additionally, I used multivariate statistical methods, like principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA), to identify the key features driving the biological differences between the datasets. This approach enabled us to gain valuable insights into the molecular pathways involved in the disease condition we were studying.

4. Question: How do you ensure the code you write for bioinformatics algorithms and pipelines is production-ready and maintainable? Answer: To ensure my code is production-ready and maintainable, I follow best software development practices. I modularize the code to make it reusable and easily maintainable. I also write unit tests to validate the correctness of the algorithms and pipelines. Version control, such as Git, is integral to track changes and collaborate effectively with the team. Moreover, I document the code extensively, including comments within the code and external documentation, to facilitate easy understanding and future updates.

5. Question: As a Bioinformatics Scientist, you’ll be working with cross-functional teams. Can you share an example of how you effectively collaborated with different stakeholders in your previous projects? Answer: In one of my previous projects, I collaborated with both data scientists and domain experts to develop a machine learning-based classifier for disease subtyping using multi-omics data. To ensure effective collaboration, I actively engaged with the domain experts to understand the biological context and the specific features relevant for disease classification. Simultaneously, I communicated the technical aspects of the machine learning models to the domain experts in a clear and accessible manner. By fostering open communication and understanding each other’s perspectives, we successfully built a robust classifier that was both scientifically sound and technically efficient.

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