Discovery Bioinformatics Methodologist Role at Syngene

Discovery Bioinformatics Methodologist Role at Syngene

Are you passionate about pushing the boundaries of science and technology to advance drug discovery and development? Syngene International Limited, a renowned global discovery, development, and manufacturing organization, is seeking innovative scientists for the Discovery Bioinformatics Methodologist role at Syngene

Designation: Research/Senior Investigator

Position: Discovery Bioinformatics Methodologist

Company: Syngene International Limited

Job Location: Bangalore

Department: Informatics and Predictive Sciences (BBRC-IPS)

About Syngene :

Incorporated in 1993, Syngene International Ltd. is an innovation-focused global discovery, development and manufacturing organization providing integrated scientific services to the pharmaceutical, biotechnology, nutrition, animal health, consumer goods and specialty chemical industries around the world. Syngene’ s clientele includes world leaders such as Bristol-Myers Squibb, Baxter, Amgen, GSK, Merck KGaA and Herbalife. Its innovative culture is driven by the passion of its 4240- strong team of scientists who work with clients from around the world to solve their scientific problems, improve R&D productivity, speed up time to market and lower the cost of innovation.

Job Purpose :

• We are seeking innovative scientists for Research Scientist / Senior Research Scientist position to join our Informatics and Predictive Sciences (BBRC-IPS) group that is a part of main Discovery Biology and Translational Sciences (DBTS) team.
• The group works on real-world health data, design experiments and analyze both pre-clinical and clinical -omics data sets, including Exome, Whole Genome Sequencing, RNA-Seq, single-cell sequencing, high-throughput proteomics, multiplex flow cytometry, CRISPR etc. to nominate novel drug targets, enable patient enrichment strategies, and guide drug development decisions.

Key Responsibilities:

• Identify and implement state-of-the-art statistical methods for data exploration, visualization, analysis, and integration of cancer genomics/epi-genomics, and other forms of high-dimensional – omics data for patient clinical outcomes
• Develop and evaluate performance of existing or new assays through statistical inferences
• Interpret quality control data metrics in NGS methodology and communicate effectively with the team and the respective stakeholders
• Participate in experimental design and determine strategies for balanced representation of samples
• Perform biomarker selection and stratification through strong statistical based evidence
• Collaborate closely with others on translational research teams to evaluate, develop, and apply cutting-edge methods for analysis of multi-modal, high-dimensional -omics data

Technical/functional Skills:

• Expertise in algorithmic implementation, statistical programming, and data manipulation, using e.g., R or Python, and contemporary, open-source bioinformatics tools and database structures
• Solid grounding in statistical theory and familiarity with recent developments in statistics
• Skilled at working with large omics data sets (transcriptomic, genomic, proteomic, and/or epigenomic data), whole genome sequencing, whole exome sequencing.
• Strong experience in cancer genomics and epi-genomics is required
• Proficient with high-performance computing environments like cloud computing
• Working knowledge of workflow languages for example: CWL or Nextflow

Behavioural Skills :

• Strong problem-solving and collaboration skills, and rigorous and creative thinking
• Excellent written and oral communication skills, including an ability to discuss and explain complex ideas with computational scientists, experimentalists, and clinicians
• The ability to work across organizations to define and solve problems that will benefit the whole.
• Capable of establishing strong working relationships across the organization.
• Enjoy collaborating to solve challenging problems at the intersection of modern statistics and medicine to help bring new medicines to patients.

Educational Qualification:

Ph.D. or master’s degree in Biotechnology, Bioinformatics, Statistics, Computer or Data Science, Computational Biology, or related technical discipline

Experience :

• 5+ years of experience in pharma/biotech research environment preferable
• 5+ years of experience in university, hospital or biotechnology environments

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Here are some interview questions and sample answers for the position of Discovery Bioinformatics Methodologist at Syngene International Limited:

1. Question: Can you describe your experience in working with high-dimensional -omics data and how you have utilized it in your previous research? Answer: In my previous role as a Senior Research Scientist at a biotechnology firm, I extensively worked with various -omics data sets, including transcriptomics, genomics, and proteomics. I applied statistical methods and bioinformatics tools to analyze these data sets and identify potential biomarkers for drug target nomination and patient stratification. For instance, I successfully utilized RNA-Seq data to identify differentially expressed genes in cancer samples, leading to the discovery of novel drug targets. Additionally, I have experience with whole genome sequencing and have used it to explore genetic variations associated with disease phenotypes.

2. Question: How do you approach experimental design to ensure a balanced representation of samples in your research? Answer: Experimental design is a critical aspect of omics data analysis. To ensure a balanced representation of samples, I carefully consider factors such as patient demographics, disease subtypes, and other relevant variables. I employ statistical methods like stratified random sampling to ensure that each subgroup is adequately represented in the data set. Additionally, I collaborate closely with translational research teams to gain insights into the biological context and to identify potential confounding variables that need to be controlled during the experimental design process.

3. Question: Can you explain your experience with biomarker selection and how you establish strong statistical evidence for their relevance? Answer: Biomarker selection is an important step in precision medicine and drug development. In my previous projects, I have used various statistical approaches such as differential expression analysis, machine learning algorithms, and statistical tests for association to identify potential biomarkers. I evaluate these biomarkers based on multiple criteria, including statistical significance, effect size, and validation across independent cohorts. To establish strong statistical evidence, I employ techniques like multiple hypothesis correction to control false discovery rates and cross-validation to assess the robustness of the biomarker candidates.

4. Question: How have you collaborated with experimentalists, clinicians, and computational scientists in your previous work? How do you effectively communicate complex ideas with these diverse teams? Answer: Collaboration is essential in interdisciplinary research, and I have experience collaborating with various teams. I believe effective communication is the key to successful collaboration. I make sure to present my findings in a clear and concise manner, avoiding jargon when communicating with experimentalists and clinicians. For computational scientists, I provide detailed explanations of the statistical methodologies and algorithms used in my analyses. Additionally, I actively participate in group meetings, where I encourage open discussions to exchange ideas and address questions from diverse team members.

5. Question: How do you stay updated with the latest developments in statistics, bioinformatics, and computational biology? Answer: As a methodologist, staying up-to-date with the latest developments is crucial. I regularly attend scientific conferences, workshops, and webinars focused on bioinformatics and statistics. I also follow leading research journals in the field to keep track of recent publications and breakthroughs. Furthermore, I am an active member of online forums and scientific communities, where I can engage in discussions with peers and experts in the field. This continuous learning approach ensures that I stay informed about cutting-edge techniques and methodologies.

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