Build strong partnerships with SPD experimentalists, process engineers, and analytical scientists to gather requirements for data solutions which will have direct pipeline impact.
Design and implement robust, scalable data pipelines that ingest experimental and process data from SPD teams.
Deliver analysis-ready datasets to support SPD digital initiatives, including process modeling and Bayesian optimization.
Define and enforce data standards, metadata schemas, and ontologies that make SPD data interoperable and readily consumable by downstream modeling and optimization workflows.
Automate data ingestion from laboratory instruments, electronic lab notebooks, PAT systems, and manufacturing systems and integrate with cloud-based storage and compute environments.
Apply and generate data analysis and visualization workflows.
Design and develop dashboards, reports, and data exports.
Curate data and support definition of needs for automation of data ingestion.
Influence digital data strategy for SPD by identifying opportunities to improve data capture practices at the source and reduce friction between experimentation and modeling.
Demonstrate excellent interpersonal, communication, and collaboration skills.
Embrace and model our core values including fostering a supportive culture where all can thrive.
Collaborate effectively in a dynamic, integrated, and multidisciplinary team environment.
Perform impactful scientific innovation in a team-oriented manner that builds trusted partnerships across vast stakeholder networks.
Publish and present research, including maintaining an established track record of interaction with the broader academic community.
Education Minimum Requirement:
Ph.D. in Computer Science, Data Science, Engineering, Chemistry, Physics, Biology, Pharmaceutical Sciences, or a closely-related field with at least 3 years of industrial/pharmaceutical or relevant experience.
M.S. in Computer Science, Data Science, Engineering, Chemistry, Physics, Biology, Pharmaceutical Sciences, or a closely-related field with at least 5 years of industrial/pharmaceutical or relevant experience.
B.S. in in Computer Science, Data Science, Engineering, Chemistry, Physics, Biology, Pharmaceutical Sciences, or a closely-related field with at least 7 years of industrial/pharmaceutical or relevan
Benefits
Health insurance
Additional Information
Job Description
Associate Principal Scientist, Data Engineer, Digital Insights, DSCS Digital Technologies
We are a global biopharmaceutical leader with a portfolio of prescription medicines, oncology, vaccines and animal health products. We are driven by our purpose to develop and deliver innovative products that save and improve lives. With 69,000 employees operating in more than 140 countries, we offer state-of-the-art laboratories, plants and offices that are designed to inspire our employees as we learn, develop and grow in our careers. We are proud of our over 125 years of service to humanity and continue to be one of the world's biggest investors in Research & Development.
We are seeking an Associate Principal Scientist to join our Digital Insights team within the Development Sciences and Clinical Supply (DSCS) Digital Technologies organization. Digital is the multiplier that will allow DSCS to deliver better experiments faster, efficient filing and launch, more robust supply chains and higher-confidence decisions across the portfolio.
The DSCS Digital Technologies organization is responsible for the invention and application of new digital tools/workflows to support scientists across drug substance development, drug product development, and analytical development. We aspire to embed digital technologies into the fabric of DSCS culture to drive transformational impact. The tools that we develop are the teams developing them, and in this Associate Principal Scientist role, the successful candidate will design, build, and maintain data pipelines that capture, curate, and deliver experimental and process data from Sterile Product Development (SPD) teams into downstream SPD digital initiatives, including mechanistic and data-driven process modeling as well as Bayesian optimization approaches. The pipelines built here will directly enable colleagues within Digital Insights to deploy a variety of modeling approaches for de-risking and optimizing sterile drug substance (DS) and drug product (DP) manufacturing processes across biologics and vaccines.
The ideal candidate will have hands-on experience in sterile drug product development and will have transitioned into a data engineering or data science role. This domain depth enables the candidate to work comfortably alongside SPD experimentalists, understand the scientific context of the data being captured, and anticipate the data needs of downstream modelers. Preferred is familiarity with mechanistic and data-driven modeling approaches so that the candidate can effectively support modelers by delivering data in the right format, granularity, and context.
Merck · - New Jersey