Note: The job is a remote job and is open to candidates in USA. CAS is seeking a Manager, Scientific AI Engineering & Data Science to lead and grow a team developing AI and data science systems that support scientific discovery products and internal platforms. The role focuses on hiring, coaching, career development, team health, cross-functional partnership, and maintaining sufficient technical fluency to guide AI engineering and data science work.
Responsibilities
- Own hiring for a growing team — sourcing, recruiting, interviewing, and evaluating talent
- Develop, retain, and motivate data scientists and AI engineers with scientific domain depth; shape and build the team
- Coach and mentor across levels, supporting both technical growth and career progression
- Manage performance and career development in line with the DAI career framework — job family, scope tier, and depth/breadth path
- Build bench strength, support succession, and sustain a healthy, inclusive, high-expectation team culture
- Match people to work thoughtfully, balancing team delivery with individual growth and job satisfaction
- Maintain enough fluency across modern AI engineering — LLMs, agentic workflows and tool use, RAG, retrieval and extraction over scientific content, and evaluation — to lead and coach the team credibly
- Judge the quality of the team's technical work well enough to give meaningful feedback and guide development
- Understand the trustworthy-AI principles the team works to — including CAS's reliance on curated, provenanced scientific content, and the difference between acceptable model variability and genuine failure — well enough to reinforce them
- Partner with technical leads and product, who own technical direction, architecture, and roadmap
- Ensure the team is well-resourced, unblocked, and set up to succeed, working with technical leads and product on prioritization and staffing
- Remove organizational and people-level obstacles, and escalate and resolve issues that slow the team
- Support healthy operating practices — delivery rhythm, review, and production health — without owning roadmap outcomes
- Bring team-wide and enterprise-wide thinking to the role, in service of scaling and elevating the organization aggressively
- Step in to lead and drive cross-cutting initiatives as priorities dictate — for example, specific programs with internal partners or targeted team-elevation efforts — unconstrained by the manager's own set of direct reports
- Speak fluently and confidently about the team's work to both internal and external audiences, including customers, partners, and the broader scientific community
- Approach the role with an ownership mindset that extends beyond the immediate team to the broader organization's success
- Partner across Product, Technology, Content Operations, and other teams as the people leader for the team
- Represent the team's capacity, needs, and health to stakeholders and leadership
- Connect the team's people and capabilities to CAS's broader goals
Skills
- Master's degree in a relevant technical or quantitative discipline (e.g., Computer Science, Applied Mathematics, Statistics, Data Science, Computational Chemistry, Physics, Bioinformatics), or equivalent experience
- 8+ years of relevant experience, including 3-5+ years developing people and leading technical teams
- Enough hands-on background in AI/ML engineering and data science to lead and coach the work credibly; direct roadmap or delivery ownership is not required at this level
- Familiarity with modern AI engineering — LLM-based, agentic, and large-scale retrieval and extraction systems
- Proven ability to hire, coach, grow, and retain technical talent
- Strong people-management, feedback, and career-development skills
- Team-wide and enterprise-wide perspective, and readiness to lead initiatives beyond one's own reporting line
- Ability to represent the team's work fluently and confidently to internal and external audiences
- Sound judgment on team health, culture, and prioritization
- Sufficient technical fluency to earn the trust of a team of data scientists and AI engineers
- A PhD and/or deep scientific domain expertise (chemistry, life sciences, materials science) is valued as a capability the person brings, and is not required
- Experience in scientific, chemical, pharmaceutical, or materials-science domains is desired
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