Key Responsibilities

• Translate ambiguous clinical and operational problems into well-scoped AI solutions, from problem framing and data assessment through prototype, validation, and production deployment.
• Design and implement MLOps/LLMOps pipelines to deploy, monitor, and manage large language models in production environments, following software engineering best practices
• Collaborate with data scientists to deploy and/or fine-tune high-performing Generative AI models, and apply modern techniques from relevant published work where appropriate.
• Develop scalable and robust data and ML pipelines for ingestion, preprocessing, validation, training, evaluation, and model deployment across the clinical development ecosystem.
• Evaluate and recommend AI tools and frameworks to meet clinical and operational requirements, including decisions around retrieval-augmented generation (RAG), vector databases, embedding models, and LLM providers, balancing compliance, performance, and cost.
• Develop, deploy, and maintain robust data pipelines for structured and unstructured clinical data, integrating internal systems with CRO and external partner data sources, and ensuring end-to-end data integrity and traceability.
• Identify and implement opportunities to increase data interoperability and standardization across Global Development systems and other business units, reducing manual effort and accelerating data availability for clinical programs.
• Develop and implement automated quality monitoring pipelines for both internal and CRO-sourced clinical data, surfacing quality metrics and triggering corrective workflows in alignment with the study’s Medical Monitoring Plan.
• Ensure all data solutions comply with applicable regulatory frameworks including HIPAA, GDPR, and 21 CFR Part 11, and contribute to data governance strategy, data lineage documentation, and audit-readiness.
• Maintain and manage code repositories (e.g., Bitbucket, GitHub) with clean, well-documented, version-controlled code; uphold engineering best practices including code review, testing, and CI/CD pipelines.

Qualifications

• Advanced degree in computer science, biomedical informatics, statistics, or a closely related field required; PhD with 6+ years of relevant experience or MS with 10+ years of relevant experience strongly preferred.
• Minimum of 5–7 years of experience designing, implementing, and leading data engineering solutions in life sciences or healthcare, with demonstrated accountability for end-to-end delivery is required.
• Demonstrated expertise in designing and maintaining clinical or biomedical data infrastructure, including data lake and warehouse architectures optimized for regulatory-grade clinical data.
• Expertise in modern cloud data platforms (Snowflake, Databricks, Redshift, BigQuery) and proficiency in Python, SQL, R, and related programming languages.
• Proficiency in cloud architecture (AWS, Azure, or GCP) and DevOps practices including CI/CD, containerization (Docker/Kubernetes), and infrastructure-as-code; relevant certifications a plus.
• Demonstrated experience building, scaling, and maintaining pipelines for structured and unstructured data, with the ability to integrate pipelines across the enterprise.
• Deep knowledge of regulatory frameworks (HIPAA, GDPR, 21 CFR Part 11) and clinical data standards (CDISC, HL7, FHIR), with experience applying them in regulated development environments.
• Hands-on experience developing or integrating machine learning pipelines and working with clinical AI/ML applications such as natural language processing, anomaly detection, or predictive modeling.
• Strong scientific communication skills, with the ability to translate complex technical architectures and outputs into clear strategic recommendations for non-technical clinical and executive stakeholders.
• Experience with data governance frameworks, data quality tooling, and metadata management practices in clinical or regulated settings.

Ideal Candidate Trail

• A builder at heart — someone who moves fluidly from whiteboard to working solution, comfortable owning the full arc from idea through deployed product.
• Able to assess a clinical or operational challenge and independently determine whether and how AI can meaningfully solve it — not just implement what's handed to them.
• A natural cross-functional collaborator who earns the trust of clinical, operations, regulatory, and IT stakeholders alike—comfortable operating as both a technical lead and a strategic partner.
• Solutions-oriented and innovation-driven, with the confidence to constructively challenge legacy thinking and the pragmatism to deliver within the constraints of a regulated environment.
• Thrives in ambiguity and fast-moving environments, able to balance long-horizon architectural thinking with near-term delivery commitments across multiple concurrent transformation workstreams.
• A proactive learner who actively monitors advances in AI/ML, data engineering, and clinical informatics and brings external insights back to accelerate the program’s evolution.

Exact Compensation may vary based on skills, experience and location.

Pay range

$234,000

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$305,000 USD