Job at a glance
Description and Requirements Job Description Pharma R&D Data & AI StrategistWho You'll Work WithSlalom is seeking a Pharma R&D Data & AI Strategist to help pharmaceutical and biotechnology clients translate ambitious scientific, business, Data, and AI strategies into measurable R&D outcomes. You will work alongside senior R&D leaders, research scientists, translational scientists, clinical development teams, data and AI leaders, enterprise architects, product teams, and technology partners to modernize how data and AI enable the discovery and development of new therapies.You will collaborate with multidisciplinary Slalom teams spanning Life Sciences, Data & AI, Strategy, Organizational Change, Product, and Technology to help clients move from fragmented scientific and clinical data and isolated AI experimentation toward scalable, governed, reusable enterprise capabilities.The role spans the R&D lifecycle, with opportunities across Research & Discovery, Translational Science, Preclinical Development, Clinical Development, Clinical Operations, Safety, Regulatory, CMC, and R&D Portfolio Management.What You'll DoDevelop Data & AI strategies for pharmaceutical R&D, connecting scientific and business priorities to actionable roadmaps across Research and Development.Partner with R&D executives, scientists, and functional leaders to identify and prioritize high-value Data & AI opportunities based on scientific impact, business value, feasibility, data readiness, risk, and organizational readiness.Shape Data & AI strategies across target identification and validation, disease biology, computational biology, medicinal chemistry, molecular design, assay and screening sciences, translational research, biomarker development, preclinical development, clinical development, and trial operations.Help clients unlock value from complex Research data, including compound and molecular data, chemical structures and properties, assay results, experimental and ELN data, genomics and other omics, imaging, biomarkers, targets, pathways, in vitro/in vivo data, literature, and other scientific knowledge.Help clients unlock value from Clinical data, including study, site, investigator, subject, visit, endpoint, laboratory, safety, operational, and other clinical trial data.Translate R&D outcomes and AI use cases into the data products, knowledge assets, platform capabilities, governance, operating models, and organizational capabilities required to deliver them.Define domain-oriented and data product strategies that improve the accessibility, interoperability, quality, discoverability, and reuse of scientific and clinical data.Establish AI-ready data foundations, including canonical models, metadata, scientific ontologies, semantic layers, knowledge graphs, data quality, lineage, and data product certification standards.Design Data & AI operating models that establish ownership, decision rights, governance, funding, product management, and effective collaboration between scientists, R&D business teams, data organizations, AI teams, and centralized technology organizations.Shape strategies for GenAI and agentic AI, including scientific copilots, research assistants, knowledge agents, molecule and target intelligence, clinical agents, and increasingly automated R&D workflows.Connect AI ambitions to enterprise architecture and modern platforms such as Databricks, Microsoft/Azure, AWS, Snowflake, scientific platforms, and specialized life sciences technologies.Facilitate executive and scientific workshops that bring together R&D, Data, AI, and Technology stakeholders around a shared vision and executable roadmap.Define business cases, value frameworks, OKRs, and KPIs that connect Data & AI investments to outcomes such as research productivity, decision quality, cycle-time reduction, probability of technical success, trial performance, and speed to patients.Translate strategy into execution through MVPs, data products, prioritized backlogs, delivery increment