Overview Associate Principal Scientist to contribute to the Inventory and Data Management team within the Biomarker Operations group in Translational Medicine. The role designs, builds, and deploys the digital infrastructure that manages biomarker specimen assets and transforms complex biomarker data into a streamlined, accessible, and intelligent ecosystem to enable data‑driven decisions across the pipeline. Responsibilities Integrate, clean, and govern disparate data sources (e.g., biorepositories, central labs, testing labs) into a cohesive data environment. Maintain accurate specimen inventory by tracking samples from clinical trials and assay metadata generated from collections. Embed automated quality control checks and validation rules into data pipelines to ensure data integrity and compliance. Support teams with reports of biomarker information using various query and database integration tools. Leverage the Microsoft Power Platform to automate specimen tracking, data QC, and reporting; build and deploy reusable automation solutions for multiple clinical trials. Design and implement robust, scalable data pipelines using Azure Data Factory and Azure SQL. Create and manage dynamic, interactive dashboards in Power BI and Fabric; develop data models and visualizations for self-service access to biomarker inventory and metadata. Maintain effective communications and relationships with project teams, vendors, and cross‑functional company teams. Qualifications Education Bachelor’s (with 12 years), Master’s (with 8 years), or Ph.D. (with 4 years) in bioinformatics, computer science, data science, engineering, data analytics, life sciences or related field and relevant experience. Required Experience and Skills A deep understanding of the clinical trial process and ability to translate scientific needs into technical solutions. Modern data stack expertise: Microsoft Power Platform (Power Automate, Power Apps, Dataverse); data engineering (SQL for complex querying, Python for data processing); database architecture (normalized relational schemas, referential integrity); cloud data solutions in Azure (Azure Data Factory, Gen1/Gen2 dataflows); advanced BI skills in Power BI (robust data models, DAX, performance optimization). Preferred Experience and Skills Experience with LIMS systems and building relational models and queries to support stakeholder decisions. Builder’s mindset with the ability to take a problem from concept to automated solution; curiosity and willingness to experiment with cloud automation and BI technologies. Advanced degree in Computer Science, Data Engineering, or Bioinformatics with applicability to real-world challenges. Hands-on experience with large‑scale data transformations using Databricks; familiarity with multi‑cloud environments (Azure and AWS S3). Interest in applying generative AI to data solutions, including experience with Copilot Studio and related tools to enable natural language querying of datasets. Experience integrating Large Language Models (LLMs) to enhance data and data-adjacent tools, and embedding LLMs as autonomous agents in backend processes. Clinical development fluency and experience with clinical systems, with preference for Veeva CTS. Familiarity with Jira and Confluence; understanding of SDLC and the rigor required for robust, compliant solutions in a validated (GxP) environment. Experience leveraging Azure developer stacks, including deploying Azure Functions for Python data processing and using Azure Logic Apps for enterprise-grade workflows with advanced monitoring. Working Arrangements and Notice Location options: Rahway, NJ or North Wales, PA based on candidate preference. US and Puerto Rico residents: company is an Equal Employment Opportunity employer. Candidates may request accommodations during the application or hiring process. This description contains standard EEO statements and information about benefits and posting details as context for applicants. #J-18808-Ljbffr Merck
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