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The Data Scientist will develop agent-based workflows and AI features for CAS products, emphasizing software development and collaboration with teams.

Part-time 6-month placement for PhD students to apply AI/ML methods to scientific problems: literature review, prototype/model design, validate results with domain experts, document and present findings.

Undergraduate intern will run experiments and evaluations for AI-for-science projects, assist with data cleaning and analysis, document findings, and present a technical report under mentorship of the Data Analytics and Insights team.

Undergraduate internship to support AI-for-science R&D projects: run experiments and benchmarks, prepare and analyze data, document results, and present technical findings under team mentorship.

People-leadership role managing and growing a team of data scientists and AI engineers building retrieval, extraction, and reasoning systems for scientific discovery products. Focus on hiring, coaching, performance management, team health, cross-team initiatives, and communicating work to stakeholders. Maintain technical fluency in LLMs, RAG, agentic workflows, and evaluation to mentor teams, while partnering with technical leads and product owners.

Lead strategic sourcing for technology-related goods and services, negotiate enterprise contracts (MSAs, SOWs, licensing), manage vendor relationships and contract lifecycle, drive cross-functional collaboration, mentor sourcing team members, conduct market research and RFPs, and present strategies to senior leadership to optimize cost and mitigate risk.

Design, develop, and maintain reusable tooling and deployment pipelines; operate and support enterprise tooling, monitoring, and cloud environments; drive automation and reliability improvements; troubleshoot incidents and provide operational support; collaborate with cross-functional teams, mentor junior engineers, and contribute to tooling strategy and process improvements.

Lead multiple Agile teams to design, build, and deliver large-scale Java/Linux applications and big-data solutions. Drive engineering excellence via automated testing, DevOps/CI, mentorship, backlog grooming, and cross-organizational influence to advance CAS products.

Design, build, automate, and support cloud infrastructure and CI/CD for production systems. Run and optimize EKS/Kubernetes at scale, implement IaC (Terraform), observability, security best practices, disaster recovery, and cost optimization. Collaborate with engineering teams, participate in on-call incident response, and produce architecture documentation and operational runbooks.

Lead execution of enterprise data governance: drive roadmap and KPIs, run a Data Steward Council, operationally own the data catalog, extend semantic models, coach stewards, and partner with engineers to improve metadata, data quality, and governance adoption across domains.