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Merck Sharp Dohme
Sr. Specialist, Technical Product Management
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What they do
A Data Manager manages databases and coordinates data collection and analysis for a company or organization. Develops procedures for documentation and data storage. Performs or manages data analysis for studies, projects and reports.
$144,914 / year median in Pennsylvania
+6% projected growth
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Sr. Specialist, Technical Product Management
West Point, PA
Posted 79 days ago
Apply Now Job Description We aspire to be the premier research-intensive biopharmaceutical company. We're at the forefront of research to deliver innovative health solutions that advance the prevention and treatment of diseases in people and animals. We are seeking a Sr. Specialist Technical Product Management with deep expertise in the drug discovery and pre-clinical research process to help build and enhance data products. Join our team to bridge the gap between scientific requirements and data-driven product development and make a significant impact on global health. Responsibilities Collaborate closely with Product, Data Science, and Engineering teams to define and develop data products tailored for drug discovery and preclinical research. Gather, manage, and prioritize product requirements by engaging with scientific stakeholders to understand user problems, technical constraints, and strategic objectives. Define and refine the product backlog; create actionable user stories that reflect the unique workflows and challenges of preclinical drug discovery. Act as a user expert, deeply understanding the motivations, pain points, and goals of scientific users. Justify and prioritize improvements to existing products, focusing on delivering measurable value to scientific research and operations. Develop and maintain technical knowledge and domain expertise in drug discovery, cheminformatics, and data-driven research processes. Write clear problem statements and requirements; facilitate solution design with engineering teams. Serve as Product Owner on Agile teams or work closely with one to ensure scientific and data needs are met. Plan, design, and conduct testing activities; compile critical training and communication content for scientific end-users. Analyze data, observations, and research to generate insights that inform product strategy and decision-making. Support the Product Manager in defining and executing strategy in collaboration with engineering teams for one or more data products. Accept completed user stories, ensuring deliverables meet acceptance criteria and scientific requirements. Act as a customer champion, articulating and advocating for the needs of scientific stakeholders. Qualifications Required Bachelor's degree in Life Sciences, Biomedical Engineering, Chemical Engineering, Computer Science, or a related field. Two or more (2+) years building tools or data assets supporting scientific or data analytics workflows. Three or more (3+) years working with scientific users (e.g., chemists) to define requirements for data products. Experience with data analytics or data science capabilities, especially in a scientific context. Experience building ERDs, logical data models, and source-target mappings for scientific data. Hands-on experience or working knowledge with data management services (AWS Athena, Glue, S3, Redshift, or similar). Proficiency in SQL and scientific data management. Ability to translate scientific/business problems into actionable requirements and tasks. Strong analytical problem-solving skills. Excellent written and verbal communication skills, with the ability to engage both technical and scientific stakeholders. Ability to work independently and manage multiple complex projects simultaneously. Preferred Advanced degree in a related field. Direct experience in pharmaceutical drug discovery and pre-clinical development. Domain knowledge or familiarity with cheminformatics, laboratory data, and scientific workflows. Knowledge of cheminformatics platforms (e.g., Pipeline Pilot, RDKit, OpenEye) and integration with data science tools. Familiarity with the drug discovery processes, including key endpoints such as potency, selectivity, ADMET properties, and lead optimization strategies. Familiarity with cheminformatics tools and concepts such as molecular descriptors, fingerprints, QSAR modeling, and chemical database management. Experience with Machine Learning Platforms (e.g., Sagemaker, DataBricks) in a scientific setting. Experience working in Agile software development. Familiarity with cloud-based software and scientific data platforms.