Senior Manager, Research Intelligence Engineer

Bristol Myers Squibb•Tampa, NJ

5d•Hybrid

About The Position

At Bristol Myers Squibb, our employees often ask, “Who are you working for?”—a question that fuels collaboration, accountability, and urgency in our work. Our purpose-driven culture inspires us to discover, develop, and deliver innovative medicines to prevail over serious diseases. We offer uniquely interesting and meaningful work, opportunities for growth, and a supportive environment that values inclusion, wellbeing, flexibility, and comprehensive benefits. This is work that transforms the lives of patients, and the careers of those who do it. Position Overview Bristol Myers Squibb seeks a hands-on, technically accomplished Senior Manager to advance Research Intelligence & data capabilities within Molecular Invention Data | R&D Data & Analytical Platforms BI&T. The Senior Manager, Research Intelligence Engineer combines Research domain analysis, business and process analysis, data engineering, and applied AI to design and deliver intelligence & research data products that improve discovery portfolio management, operational execution, scientific insights, analysis, and decision-making. Working within the Molecular Invention Data team, the broader R&D Data and Analytical Platforms, partnering with Research Business Intelligence & Technology and Research scientists, the role translates complex Research questions and workflows into scalable data, tooling, and agentic solutions. The successful candidate will possess engineering prowess and substantial analytical accountability. They will work directly with Research stakeholders to understand domain context, map decisions and processes, shape product requirements, engineer AI-enabled workflows, and ensure that solutions are grounded in reliable data and fit-for-purpose technology. Lead the technical design and delivery of reusable Research Intelligence capabilities across the Research domain, including ResearchCentral and the broader MI Data ecosystem, spanning contextual intelligence, process automation, knowledge-driven experiences, and AI-enabled decision support. The Senior Manager will provide value-focused leadership, guide engineers and analysts, and champion the adoption and consistent application of standards for responsible, observable, and reusable AI solutions, while remaining accountable for hands-on analysis, engineering, and delivery.

Requirements

Bachelor’s degree in computer science, engineering, data science, life sciences, or a related discipline; advanced degree or equivalent experience preferred.
6+ years of relevant experience designing and delivering production data, software, or analytics solutions in a complex enterprise environment.
Demonstrated hands-on experience with generative AI or agentic systems, including orchestration, retrieval, tools or APIs, evaluation, observability, and responsible AI controls.
Strong software and data engineering foundation, including Python or a comparable language, SQL, APIs, cloud data platforms, source control, testing, and CI/CD practices.
Experience eliciting requirements, analyzing processes and data, and translating domain needs into implementable technical designs.
Ability to establish credibility with scientific and technical stakeholders, navigate ambiguity, and lead complex work across organizational boundaries.
Excellent written and verbal communication, facilitation, systems-thinking, and technical mentoring skills.

Nice To Haves

Experience in pharmaceutical R&D, drug discovery, molecular invention, research portfolio operations, or an adjacent scientific domain.
6+ years of relevant experience designing and delivering production data, software, or analytics solutions in a complex enterprise environment.
Familiarity with portfolio planning, asset progression, scientific and operational metrics, and the information needs of Research decision makers.
Experience with Databricks, cloud-native architecture, knowledge graphs or semantic models, vector retrieval, model evaluation frameworks, and enterprise AI platforms.
Experience with data products, metadata, data quality, lineage, governance, and FAIR principles.
Product-oriented mindset with experience moving capabilities from prototype through production, adoption, and measurable value realization.
Curiosity, pragmatism, and the ability to learn new Research domains quickly while maintaining strong engineering discipline.

Responsibilities

Partner with scientists, portfolio teams, product owners, and operations leaders to frame ambiguous business and scientific questions as actionable intelligence opportunities.
Develop sufficient understanding of Research domains, portfolio processes, decision points, data semantics, and user needs to guide solution design.
Lead discovery, requirements analysis, process decomposition, and Process Context Mapping for AI-enabled and data-driven workflows.
Take the lead in analyzing significantly complex, unscoped Research data, workflow, and operational problems; identify business opportunities and areas of operational whitespace where data, automation, or agentic capabilities can create measurable value rather than waiting for opportunities to be predefined.
Translate domain needs into prioritized use cases, product requirements, data requirements, acceptance criteria, and measurable outcomes.
Design, prototype, engineer, and productionize agentic and generative AI capabilities that support Research portfolio management, operational excellence, and scientific decision-making.
Build AI-enabled workflows that combine enterprise data, domain context, retrieval, tools, APIs, models, and human decision points.
Implement evaluation, testing, observability, guardrails, and feedback mechanisms for reliable and responsible AI behavior.
Create reusable patterns, components, and technical documentation that accelerate adoption across Research tools and data.
Engineer and integrate the data services, semantic context, metadata, APIs, and platform components required for Research Intelligence solutions.
Develop strong hands-on expertise in Databricks as the organization’s data intelligence platform, and work across ResearchCentral, Research data products, source systems, and enterprise tooling to deliver secure, maintainable end-to-end solutions.
Apply sound software and data engineering practices, including modular design, version control, automated testing, CI/CD, monitoring, and production support.
Partner with data architecture, data engineering, platform, security, and governance teams to align solutions with enterprise standards and FAIR data principles.
Own value workstreams from discovery and architecture through iterative delivery, validation, release, adoption, and operational support.
Develop roadmaps and delivery plans that balance near-term experimentation with scalable, supportable enterprise capability.
Define success measures and use evidence from adoption, quality, cycle time, user feedback, and decision impact to improve solutions.
Identify delivery risks, dependencies, and data or process gaps early, and coordinate resolution across teams.
Support change, documentation, training, and adoption so that technical capabilities become sustained Research practices.
Serve as a technical partner to Research leaders, product owners, domain experts, architects, engineers, data scientists, and external delivery teams.
Communicate complex domain and technical concepts clearly across scientific, product, engineering, and leadership audiences.
Provide design guidance, code and solution reviews, troubleshooting support, and technical mentorship to engineers and analysts.
Contribute to investment recommendations by clarifying expected value, feasibility, data readiness, delivery effort, and operational risk.
Help build a community of practice for Research Intelligence and agentic engineering across Research, BI&T, and R&D Data ecosystem.
Adopt, apply, and champion standards for AI orchestration, evaluation, governance, observability, Process Context Mapping, and capability reuse.
Ensure that sensitive Research data, model outputs, tool access, and human approvals are handled through appropriate controls and traceability.
Promote common patterns for contextual intelligence, knowledge retrieval, workflow orchestration, and integration rather than isolated point solutions.
Capture lessons, reference architectures, and reusable assets that improve delivery speed and reduce technical and institutional knowledge risk.

Benefits

wellbeing support
retirement and financial protection benefits
insurance offerings (medical, dental, vision, life and disability)
Flexible Time Off (FTO)
11 paid company holidays each year
160 hours of paid vacation annually for new hires
3 optional holidays
paid sick leave
up to two paid volunteer days per year
summer hours flexibility
leaves of absence for medical, personal, parental, caregiver, bereavement, or military needs
annual Global Shutdown between Christmas Day and New Year's Day