Senior Scientific Data Engineer

Role Overview

• Partner with discovery, translational, clinical, computational, and portfolio scientists to identify high-value research opportunities for AI and translate them into AI-tractable specifications with measurable scientific success criteria.
• Architect, build, and operationalize agentic and multi-agent workflows for complex scientific tasks such as target evidence assembly, indication rationale construction, biomarker interpretation, translational synthesis, literature and evidence triangulation, and decision support.
• Design reusable AI capabilities, including agentic frameworks, evaluation harnesses, MCP-enabled tool integrations, prompt and policy libraries, and research workflows intended for adoption across multiple programs rather than single-use deployments.
• Establish rigorous evaluation methodologies for AI-enabled research outputs and upstream data/tool quality, including expert-reviewed benchmarks, rubric-based assessments, grounding checks, uncertainty characterization, provenance review, reproducibility criteria, longitudinal monitoring, and failure-mode analysis.
• Operate with urgency and tight scientific feedback loops, converging with scientists on working artifacts quickly and pivoting based on direct evidence, evaluation results, and stakeholder input.
• Collaborate with engineering, data, IT, security, legal, vendor, and platform teams to ensure AI capabilities are designed with appropriate governance, integration paths, productionization patterns, and credible scaling plans from inception.
• Communicate AI opportunities, risks, limitations, evaluation outcomes, and recommended decisions clearly to scientific, technical, and executive audiences; contribute to internal standards, reference implementations, and documentation.
• Stay current with developments in agentic systems, evaluation methodology, reasoning models, retrieval, grounding, biomedical AI tooling, and research informatics, and assess where they create practical value for Research.

Requirements

• Bachelor's Degree in computational biology, bioinformatics, genetics, biology, chemistry, pharmaceutical sciences, data science, scientific computing, or a closely related field and 7+ years of academic / industry experience
• Or Master's Degree in computational biology, bioinformatics, genetics, biology, chemistry, pharmaceutical sciences, data science, scientific computing, or a closely related field and 5+ years of academic / industry experience
• Or PhD in computational biology, bioinformatics, genetics, biology, chemistry, pharmaceutical sciences, data science, scientific computing, or a closely related field and 2+ years of academic / industry experience
• Demonstrated track record translating complex scientific questions into AI-enabled workflows, reusable tools, evaluation frameworks, data products, or decision-support capabilities that scientists can evaluate, trust, and defend.
• Substantive hands-on experience with modern AI and large language model methods, including agentic workflows, multi-agent orchestration, GraphRAG, scaled tool use, and MCP patterns.
• Demonstrated experience designing and operating scientifically rigorous evaluation frameworks for AI systems, including curated benchmark datasets, expert-reviewed reference standards, rubric-based assessments, calibration and uncertainty metrics, and regression gating that has prevented quality drift in production.
• Working fluency in at least one scientific domain relevant to drug research and development
• including target biology, translational science, computational biology, clinical development, molecular invention, and biomarker science
• sufficient to engage scientists as a peer on questions of evidence and interpretation.
• Experience with knowledge graphs, biomedical ontologies, evidence models, disease models, gene/target models, and graph-based reasoning over heterogeneous biomedical evidence, including genetic associations, clinical outcomes, literature, omics data, assay data, and real-world data.
• A demonstrated bias toward urgency and tight iteration: a track record of converging on working artifacts in days, iterating directly with scientific stakeholders, and pivoting decisively based on scientific feedback.
• Sound engineering judgment regarding when to reuse existing platform components, when to extend them, when to maintain a prototype, and when to transition a capability to platform engineering for productionization.
• Excellent written and verbal communication skills and a demonstrated ability to align scientific, technical, and executive audiences around practical AI opportunities and evaluation results.

Tech Stack

• Assembly

Benefits

• Health Coverage: Medical, pharmacy, dental, and vision care.
• Wellbeing Support: Programs such as BMS Well-Being Account, BMS Living Life Better, and Employee Assistance Programs (EAP).
• Financial Well-being and Protection: 401(k) plan, short
• and long-term disability, life insurance, accident insurance, supplemental health insurance, business travel protection, personal liability protection, identity theft benefit, legal support, and survivor support.
• Work-life benefits include: Paid Time Off US Exempt Employees: flexible time off (unlimited, with manager approval, 11 paid national holidays (not applicable to employees in Phoenix, AZ, Puerto Rico or Rayzebio employees) Phoenix, AZ, Puerto Rico and Rayzebio Exempt, Non-Exempt, Hourly Employees: 160 hours annual paid vacation for new hires with manager approval, 11 national holidays, and 3 optional holidays Based on eligibility*, additional time off for employees may include unlimited paid sick time, up to 2 paid volunteer days per year, summer hours flexibility, leaves of absence for medical, personal, parental, caregiver, bereavement, and military needs and an annual Global Shutdown between Christmas and New Years Day. All global employees full and part-time who are actively employed at and paid directly by BMS at the end of the calendar year are eligible to take advantage of the Global Shutdown.