Key skills
AWSAzureCloudETLGoogle Cloud PlatformJavaPythonSQLRJupyterELTData EngineeringGCPGoogle CloudCommunicationCollaboration
About this role
Role Overview
- 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
Requirements
- 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 Computer Science, Data Science, Engineering, Chemistry, Physics, Biology, Pharmaceutical Sciences, or a closely-related field with at least 7 years of industrial/pharmaceutical or relevant experience
- Proficient in Python and/or another programming language (e.g., Java, R)
- Comfortable working in development environments such as Posit/RStudio/Jupyter
- Solid SQL skills with hands-on experience writing and optimizing queries against relational databases
- Experience with ETL/ELT (Extract, Transform, Load) processes and building data pipelines in a scientific or pharmaceutical context
- Familiarity with cloud platforms (AWS, Azure, or GCP) for data storage, processing, and integration
- Prior hands-on experience in sterile drug product development, sterile DS and DP manufacturing processes, or closely related pharmaceutical development — with a demonstrated transition into a data engineering, data science, or computational role
- Working knowledge of how mechanistic and data-driven models consume and depend on experimental data — sufficient to anticipate modeler needs and deliver appropriately structured datasets
Tech Stack
- AWS
- Azure
- Cloud
- ETL
- Google Cloud Platform
- Java
- Python
- SQL
Benefits
- medical, dental, vision healthcare and other insurance benefits (for employee and family)
- retirement benefits, including 401(k)
- paid holidays
- vacation
- compassionate and sick days