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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data & AI Strategist (Pharma R&D) - **Company:** Slalom, LLC - **Location:** Chicago, IL, United States - **Salary:** $163,000.0 - $199,500.0 - **Contract:** Temporary contract - **Skills:** Artificial Intelligence, Amazon Web Services, Data Analysis, Microsoft Azure, Clinical Data Management, Clinical Data Repository, Computational Biology, Data Governance, Graph Database, Laboratory Information Management Systems, Machine Learning, Metadata, Technical Data Management Systems, Snowflake, Data Layers, Virtual Agents, GXP, Databricks - **Published:** September 13, 2026 - **Apply:** https://www.jofdav.com/jobs/59681374-data-ai-strategist-pharma-r-d ## About the Role * Significant experience in pharmaceutical, biotechnology, life sciences R&D, or consulting to R&D organizations. * Strong understanding of the pharmaceutical R&D lifecycle, with depth in Research & Discovery and/or Clinical Development. * Experience working with Research and/or Clinical data, with the ability to understand the unique challenges associated with scientific and regulated data. * For Research-oriented candidates, experience with one or more data types such as compound/molecular, chemistry, assay, experimental, ELN/LIMS, target, genomics/omics, imaging, biomarker, translational, preclinical, or scientific knowledge data. * For Development-oriented candidates, experience with one or more data types such as clinical trial, study/site, subject, endpoint, laboratory, safety, operational, regulatory, or real-world data. * Demonstrated experience developing and operationalizing enterprise Data, Analytics, and/or AI strategies. * Ability to connect emerging AI capabilities to meaningful scientific and business decisions, rather than approaching AI as a technology-first initiative. * Experience with modern data concepts including data products, domain-oriented architectures, canonical models, metadata, ontologies, semantic models, knowledge graphs, and enterprise data governance. * Understanding of how structured, semi-structured, unstructured, and multimodal scientific data can be prepared for analytics, machine learning, GenAI, and agentic AI. * Familiarity with R&D considerations such as scientific provenance, intellectual property, FAIR principles, GxP, privacy, validation, traceability, responsible AI, and appropriate data use, as applicable to the candidate's area of expertise. * Experience defining operating models, organizational structures, governance frameworks, and decision rights within complex global organizations. * Strong executive communication, workshop facilitation, storytelling, and stakeholder-management capabilities. * Ability to operate effectively with both scientific stakeholders and deeply technical Data & AI teams. * Experience translating strategy into execution through use-case portfolios, data products, MVPs, roadmaps, backlogs, OKRs, and value realization. ## Description Slalom 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 Do * Develop 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 increments, governance gates, and implementation roadmaps. * Advise leaders on organizational readiness, skills, adoption, and change required to embed Data & AI into scientific and operational ways of working. ## Related Videos - [Blueprints for Success: Steering a Global Data & AI Architecture](https://www.wearedevelopers.com/videos/1577-blueprints-for-success-steering-a-global-data-ai-architecture) - [A Data Mesh needs Open Metadata](https://www.wearedevelopers.com/videos/505-a-data-mesh-needs-open-metadata) - [How Cisco embraced a DevOps culture within its network engineering team](https://www.wearedevelopers.com/videos/99-how-cisco-embraced-a-devops-culture-within-its-network-engineering-team) - [Hacking AI at the Edge of the Indian Ocean](https://www.wearedevelopers.com/videos/100177-hacking-ai-at-the-edge-of-the-indian-ocean) - [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail) - [Crafting Custom Frameworks with Rust: A Deep Dive into Procedural Macros](https://www.wearedevelopers.com/videos/849-crafting-custom-frameworks-with-rust-a-deep-dive-into-procedural-macros) ## Related Articles - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production) - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [Graph and AI Trends 2026: Why Is AI Running but Not Yet Delivering?](https://www.wearedevelopers.com/magazine/680-graph-and-ai-trends-2026-why-is-ai-running-but-not-yet-delivering) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Stephan Gillich - Bringing AI Everywhere](https://www.wearedevelopers.com/magazine/489-stephan-gillich-bringing-ai-everywhere)