Associate Director, Data Engineer, DSCS Digital Technologies

Merck • Upper Gwynedd Township, PA

12h • $129,000 - $203,100 • Hybrid

About The Position

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 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 Director 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, more 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 and 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. In this Associate Director role, the successful candidate will design, build, and maintain the data foundation that powers data-driven modeling of sterile drug product development. Specifically, the candidate will capture, curate, and deliver experimental and process data from Sterile Product Development (SPD) teams into machine learning, statistical, and hybrid modeling workflows. This role is a critical enabler of our process modeling strategy: using data-driven models, supported by mechanistic understanding where appropriate, to de-risk and optimize sterile drug substance (DS) and drug product (DP) manufacturing processes across biologics and vaccines.
The ideal candidate has hands-on experience in sterile drug product development and has since 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 needs of downstream modelers. Familiarity with data-driven modeling approaches, and how they consume experimental data, is strongly preferred, so that the candidate can deliver data in the right format, granularity, and context.

Requirements

• 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.
• Experience developing and deploying data pipelines, ETL/ELT workflows, and data integration solutions in a scientific or pharmaceutical context.
• Proficiency programming in Python and/or R, and with Posit/RStudio/Jupyter.
• Working knowledge of how data-driven models consume and depend on experimental data, sufficient to anticipate modeler needs and deliver appropriately structured datasets.
• Excellent communication, creativity, and interpersonal skills.
• Proven ability to deliver complex solutions under compressed timelines in a dynamic environment.
• Ability to work in a team environment with cross-functional interactions.
• Motivation to learn new skills, willingness to take on new challenges, and scientific curiosity.

Nice To Haves

• Experience with one or more drug modalities developed internally, such as small molecules, biologics, vaccines, peptides, or drug conjugates.
• Experience with sterile CMC development workflows, particularly unit operations such as mixing, pooling, pumping, filling, filtration, and freeze-drying.
• Familiarity with data-driven modeling approaches (e.g., machine learning and statistical models), not necessarily as a modeler, but understanding input/output data requirements and validation data needs.
• Experience with common process and analytical capabilities used in pharmaceutical development.
• Familiarity with laboratory data systems (ELN, LIMS, historian/SCADA systems, PAT) and how to extract structured data from them.
• Experience with data visualization tools (Shiny, Streamlit, Spotfire, Dash, Power BI, or Tableau).
• Experience connecting to AWS (S3, Redshift, Glue, Athena, SageMaker) as a data source for data visualization.
• Experience with data pipeline tools such as Dataiku or Databricks.
• Experience with relational databases, graph databases, and SQL.
• Knowledge of regulatory expectations relevant to sterile products and model-informed development (e.g., ICH Q8 through Q12, process validation, data integrity).
• Evidence of cross-functional collaboration spanning laboratory, manufacturing, modeling, and digital teams.
• Prior contributions to technology transfer, process robustness assessments, or troubleshooting in a sterile manufacturing context.

Responsibilities

• Build strong partnerships with SPD experimentalists, process engineers, and analytical scientists to gather requirements for data solutions that directly feed modeling pipelines.
• Design and implement robust, scalable data pipelines that ingest experimental and process data from SPD teams (e.g., unit operations such as mixing, pooling, pumping, filling, filtration, and freeze-drying, along with analytical characterization data).
• Partner directly on process modeling to translate data-driven model requirements into analysis-ready, feature-rich datasets tailored to modeling needs.
• Define and enforce data standards, metadata schemas, and ontologies that make SPD data interoperable and readily consumable by data-driven modeling workflows.
• Automate data ingestion from laboratory instruments, electronic lab notebooks, PAT systems, and manufacturing systems, and integrate with cloud-based storage and compute environments.
• Develop data analysis and visualization workflows that surface insight from SPD data.
• Design and build dashboards, reports, and data exports for scientific and cross-functional stakeholders.
• Curate data and define requirements for automating data ingestion at scale.
• Influence the digital data strategy for SPD, identifying opportunities to improve data capture at the source and reduce friction between experimentation and modeling.
• Communicate and collaborate effectively across scientific, engineering, and digital disciplines.
• Embrace and model our core values of inclusion, fostering a supportive culture where all can thrive.
• Collaborate productively in a dynamic, integrated, and multidisciplinary team environment.
• Deliver impactful scientific innovation in a team-oriented manner that builds trusted partnerships across broad stakeholder networks.

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

What This Job Offers

• Job Type: Full-time
• Career Level: Manager
• Education Level: Ph.D. or professional degree