Data Scientist, MSAT

As a Data Scientist at Orchard Therapeutics, you'll support the MSAT organization by leading data-driven initiatives, developing a centralized manufacturing data repository, and driving insights that enhance process understanding and control. You'll be instrumental in ensuring compliant data flow, conducting data analysis for multiple MSAT activities, and working closely with analytical scientists involved in Chemistry, Manufacturing, and Control (CMC) data management.

To qualify for the Data Scientist position at Orchard Therapeutics, candidates should possess an MSc or PhD in data sciences, statistics, bioprocess engineering, bioinformatics, or a related discipline. Additionally, experience with statistical modeling, programming languages like R or Python, and familiarity with DoE applied to biological processes are essential to excel in this role.

The Data Scientist role at Orchard Therapeutics requires proficiency in programming languages such as R, Python, SQL, and SAS. These skills are crucial for manipulating data, drawing insights from large data sets, and ensuring effective data analysis for the successful manufacture of CGT products.

Yes, prior experience working in a regulated environment, particularly within the biopharma or CGT sectors, is essential for the Data Scientist role at Orchard Therapeutics. Candidates should demonstrate competence in statistical modeling, process analytics, and regulatory compliance around manufacturing data.

At Orchard Therapeutics, successful Data Scientists are expected to possess strong analytical and problem-solving skills, attention to detail, and a passion for continuous learning. The ability to communicate complex data insights to both technical and non-technical audiences and a proactive approach to improving processes are also highly valued.

When answering this question, emphasize your familiarity with compliance standards and specific projects you've worked on in regulated environments. Detail the tools you used, the types of analyses you performed, and the outcomes of those analyses.

In your response, discuss the statistical methods you have applied, such as regression analysis, hypothesis testing, or multivariate analysis, and relate them to their practical applications in the data projects you’ve handled.

Explain the frameworks and methodologies you follow to maintain data integrity and compliance, including adherence to Good Manufacturing Practices (GMP) and regulatory standards such as ICH guidelines. Share examples of how you have implemented these in previous roles.

Discuss the data visualization tools you've used, such as Tableau, JMP, or others, and give examples of projects where your visualizations effectively communicated complex data insights to stakeholders.

Choose a specific instance where you encountered a significant data challenge. Describe the problem, your analytical process, the solution you implemented, and the positive impact it had on the project or organization.

Illustrate your teamwork and collaboration skills by sharing experiences of how you effectively communicated and collaborated with professionals from different domains, focusing on achieving common goals.

Highlight your proficiency in relevant programming languages like R, Python, and SQL, explaining how each language serves specific functions in your data analysis processes.

Discuss the resources you use to keep your skills sharp, such as online courses, webinars, professional groups, or publications in the field of data science and analytics.

In your answer, describe the specific projects involving DoE you have worked on, the objectives you aimed to achieve, and the outcomes resulting from your experimental designs.

Share a systematic approach for resolving discrepancies in data findings, such as conducting further analyses, collaborating with team members for peer review, or revisiting your source data to ensure accuracy.