Associate QA Vacancy at Schrödinger – MSc Bioinformatics, Biotech, Comp Bio Apply Online

Schrödinger Associate QA Scientist Job For MSc Biotech, Comp Bio & Bioinformatics

Schrödinger Associate QA Scientist Job For MSc Biotech, Comp Bio & Bioinformatics. QA vacancy at Schrödinger for Master’s degree in Computational Biology, Bioinformatics, Biotechnology candidates. Details of the MSc Biotech QA Job is provided below. Apply now if you are interested.

Role  – Associate QA Scientist, Biologics

Location – Hyderabad

Job description – We are seeking a highly motivated and skilled Biologics QA to join our team. The successful candidate will be responsible for ensuring the quality and accuracy of our advanced Biologics computational tools and pipelines in LiveDesign. The ideal candidate should be knowledgeable in both computational biology and software quality assurance methodologies.

As a member of our Hyderabad City-based Enterprise Informatics team, you will support the expansion of LiveDesign, our well-established, industry-leading molecular design platform, to facilitate biopharmaceutical design. Product Managers in LiveDesign help our engineering teams create groundbreaking software by driving functionality design, defining success, setting and prioritizing goals accordingly, and ensuring the scientific mission drives the process. This role provides hands-on, day-to-day access to the business direction and scientific functionality of the product and industry. As an Enterprise Biotherapeutics Product Manager, you’ll advocate for

users of our software by applying the wealth of knowledge you have acquired through practical experience to bridge the information gaps between the technical, scientific, and business worlds.

Who will love this job:

A researcher or an enthusiast with experience in biologics drug discovery who is cognizant of the challenges of currently available bioinformatics software tools

What you should have:

• Some familiarity with the biologics drug discovery and development process, namely in nucleotide, peptide, antibody, antibody drug conjugate, and/or T-cell receptor based therapeutics
• Collaborative spirit and interest exploring how computational tools can facilitate discovery and design of novel biotherapeutics
• A learning mindset

Key Responsibilities:

• Work with other QA’s to identify potential errors and inconsistencies in software codes, data pipelines, and develop test plans to ensure that they meet the desired quality standards.
• Analyze test results and provide iterative feedback to developers and the product team for improving the quality and performance of model-supported biotherapeutic design tools and pipelines.
• Develop and maintain automated testing scripts and tools for LiveDesign.
• Develop and maintain documentation for testing procedures and results.
• Collaborate with cross-functional teams including product and software developers to troubleshoot and resolve issues.
• Stay up-to-date with the latest developments in biologics space and software quality assurance methodologies to ensure the team stays ahead of evolving industry needs.

Qualifications:

• A Master’s degree in Computational Biology, Bioinformatics, Biotechnology or a related field.
• Familiarity with software quality assurance methodologies and tools, such as Agile, Scrum, and Jira.
• Excellent problem-solving skills and attention to detail.
• Ability to work independently and collaboratively in a fast-paced, dynamic environment.
• Excellent written and verbal communication skills.
• Knowledge of Linux or Unix based terminal commands.

Preferred Qualifications:

• Experience in testing bioinformatics software or pipelines.
• Programming skills in Python, R, and other relevant programming languages.
• Knowledge of genomics, proteomics, or other related fields.
• Experience with cloud computing platforms such as GCP or AWS.
• Prior exposure to Schrodinger software would be greatly encouraged.

APPLY ONLINE

Hello friends, We are happy to provide you with the expected interview questions and answers below. Please go through them once you are applied for the MSc Biotech QA Job.

Question 1: Can you explain your experience with software quality assurance methodologies and their relevance in the context of biologics drug discovery? Answer: Sure. In my previous role, I’ve been actively involved in applying Agile methodologies, specifically Scrum, to ensure efficient software development and quality assurance. I understand the importance of iterative testing and feedback cycles, which are crucial for refining computational tools used in biologics drug discovery. This approach allows for continuous improvement and adaptation to the evolving needs of the industry. Additionally, using tools like Jira, I’ve been able to streamline communication between cross-functional teams, enhancing collaboration and accelerating the development process.

Question 2: Can you describe a situation where you identified errors in software codes or data pipelines? How did you develop a test plan to address these issues? Answer: Certainly. In a recent project, I detected inconsistencies in the output of a data pipeline responsible for processing antibody sequences. To address this, I collaborated with the development team to understand the underlying code and workflow. I then formulated a comprehensive test plan that involved both manual and automated testing. I designed specific test cases to cover various scenarios and inputs, ensuring the accuracy and reliability of the pipeline’s results. Through rigorous testing and close collaboration, we were able to identify and rectify the issues, improving the overall quality of the biologics design tool.

Question 3: How do you approach the development and maintenance of automated testing scripts and tools? Can you provide an example of a successful implementation? Answer: Certainly. Developing automated testing scripts requires a structured approach. First, I analyze the critical functionalities of the computational tool and identify the areas prone to errors. Then, I design test cases that cover these functionalities, creating scripts in Python that automate the execution of these tests. For instance, in a previous project, I created a testing script that simulated the interactions between antibody sequences and target antigens. This script automatically generated various test scenarios, allowing us to validate the accuracy of the tool’s predictions across a range of inputs. This approach not only saves time but also ensures consistency in testing procedures.

Question 4: How do you stay updated with the latest developments in the biologics space and software quality assurance methodologies? Answer: Staying updated in both biologics and software quality assurance is essential. I regularly follow scientific publications, attend conferences, and engage in webinars related to biologics drug discovery. This helps me stay informed about emerging trends, advancements in the field, and potential challenges that might affect the quality assurance process. For software quality assurance methodologies, I am an active participant in online communities and forums dedicated to QA professionals. Additionally, I leverage online resources and courses to learn about new methodologies and tools that can enhance our QA processes.

Question 5: Can you provide an example of a time when you collaborated with cross-functional teams to troubleshoot and resolve issues related to computational tools? Answer: Certainly. In a project involving the development of an antibody-drug conjugate design tool, we encountered unexpected performance issues that affected the accuracy of predictions. I collaborated closely with the product and software development teams to identify the root cause. We conducted in-depth discussions to understand the complexities of the tool’s algorithms and data processing pipelines. Through this collaboration, we pinpointed a bottleneck in the code responsible for handling linker chemistry. By working together, we were able to optimize the code, improve tool performance, and ensure the reliability of the predictions for antibody-drug conjugates.

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