Principal Scientist, Signals Strategy & Informatics Lead

Merck•Upper Gwynedd Township, PA

4d•$173,200 - $272,600•Hybrid

About The Position

The Principal Scientist, Signals Strategy & Informatics Lead is a scientific and digital business owner for Signals within the Digital Insights team in the Development Sciences and Clinical Supply Digital Technologies organization. The role leads Signals strategy, product roadmap, software development life cycle, adoption, and sustained value delivery, connecting scientific needs with digital product delivery and informatics capabilities. Signals is intended to support structured data capture, method and process/product performance monitoring, predictive analytics, and prospective modeling across CMC development. The successful candidate will lead Signals as a strategic capability across Development Sciences and Clinical Supply, in partnership with Discovery and Manufacturing organizations. The role establishes approaches for monitoring analytical methods, assay performance, and product and process trends; identifying variability and emerging risks; and building prospective models to support timely, confident scientific decisions. It also provides scientific and informatics leadership, technical supervision of Signals-adjacent colleagues, and cross-functional influence. The position is based primarily in Rahway, New Jersey, with a hybrid work arrangement. Domestic relocation is indicated, visa sponsorship is available, and travel requirements are 10%. The role is a regular position.

Requirements

Ph.D. in Chemical Engineering, Biochemical Engineering, Biochemistry, Engineering, Chemistry, Biology, Pharmaceutical Sciences, or a closely related field, with at least 6 years of relevant industrial, pharmaceutical, or other experience; or a master's degree in one of these fields with at least 8 years; or a bachelor's degree in one of these fields with at least 10 years.
Relevant experience in pharmaceutical, biotechnology, life sciences, or healthcare, with demonstrated impact in analytical development, laboratory informatics, scientific data management, or a related discipline.
Recognized subject matter expertise and a track record of scientific contribution, innovation, and problem-solving in analytical sciences, scientific informatics, data science, or digital product development.
Ability to independently lead complex, multidisciplinary scientific or digital initiatives from strategy and concept through implementation, adoption, and measurable value realization.
Experience as scientific lead, business owner, product owner, or capability lead for an enterprise digital platform, laboratory informatics product, or scientific data capability.
Experience defining scientific or product strategy, establishing multi-year roadmaps, setting priorities, and making risk-based decisions in a complex stakeholder environment.
Strong understanding of SDLC practices, including requirements definition, prioritization, testing, release management, change control, adoption, support, and continuous improvement.
Deep knowledge of structured pharmaceutical data, laboratory informatics systems, scientific data standards, metadata, data quality, traceability, and governed data use for analytics and modeling.
Experience designing, applying, or scientifically overseeing analytical performance monitoring, statistical trending, multivariate analysis, or related approaches to evaluate method and assay performance.
Experience developing, deploying, validating, or governing predictive and prospective models based on structured scientific data.
Ability to identify high-value scientific opportunities and translate complex workflows and business requirements into product requirements, scalable informatics solutions, and measurable outcomes.
Ability to influence without direct authority, provide technical and scientific leadership to matrixed teams, mentor colleagues, and build alignment across scientific, digital, technology, quality, and business stakeholders.
Excellent written and verbal communication skills, including communicating strategy, scientific rationale, risks, and recommendations to senior leaders and technical and non-technical audiences.
Ability to establish new ways of working in emerging or undefined areas, including developing best practices for predictive analytics and informatics solutions.
Required skills include CMC development, data analysis, data science, innovative thinking, leadership and mentoring, predictive analytics, product management strategy, product requirements, scientific modeling, scientific presentations, software development, SDLC, strategic thinking, and value realization.

Nice To Haves

Informatics
Pharmaceutical industry

Responsibilities

Champion the vision for a modern, data-rich DSCS by evolving Signals Notebook from an initial data repository into an integrated, automated data hub.
Serve as the business owner and scientific lead for Signals, accountable for capability vision, value delivery, adoption, and alignment with DSCS priorities; coordinate with other functional areas using Signals.
Define and execute the Signals strategy and multi-year roadmap in partnership with Digital Insights, DDT, scientific functions, GxP-facing teams, Quality, and Enterprise Information Technology teams.
Own the Signals software development life cycle, including demand intake, requirements gathering, prioritization, release planning, testing, deployment, lifecycle management, and continuous improvement.
Establish transparent governance, decision rights, stage gates, and success measures for Signals enhancements and new use cases.
Translate scientific and business needs into product requirements, user stories, acceptance criteria, and scalable informatics solutions.
Lead the Signals informatics strategy for structured data across drug substance, drug product, and analytical development workflows.
Develop and deploy predictive analytics capabilities, such as method performance monitoring and product trending, to identify trends, variability, method performance, emerging risks, and intervention opportunities.
Lead prospective modeling initiatives using structured Signals data to improve prediction, planning, investigations, and scientific decision-making.
Partner with laboratory scientists, analytical leaders, data scientists, statisticians, software engineers, and digital product teams to design solutions aligned with scientific workflows.
Ensure Signals data and workflows support data quality, traceability, metadata management, governance, and data reuse for analytics and modeling.
Define and monitor product, adoption, data quality, and business value metrics; use evidence and user feedback to guide roadmap decisions.
Drive change management, stakeholder engagement, training, and community-building to expand effective use of Signals across DSCS.
Partner with IT and enterprise data teams to align Signals architecture, integrations, security, support, and lifecycle practices with corporate standards.
Communicate scientific, product, and technical concepts effectively to technical and non-technical audiences.
Influence strategic decisions through data-driven recommendations, roadmap planning, and cross-functional leadership.