Position Overview

The Manager, Scientific AI Engineering & Data Science is a people-leadership role. The manager builds, grows, and leads a team of data scientists and AI engineers who develop the systems behind CAS's scientific discovery products - the retrieval, extraction, and reasoning that power CAS Newton℠ and CAS Connections, and internal platforms. The role sits in the Data Analytics & Insights (DAI) organization.

The manager's primary work is people: hiring, coaching, developing, and retaining data scientists and AI engineers, and creating the conditions for the team to do its best work. Technical direction, architecture, and roadmap delivery are owned by technical leads and product partners. The manager is expected to carry enough technical fluency to lead, coach, and mentor credibly - to understand the work, judge the quality of an engineer's contributions, and guide growth - but is not accountable for owning the technical roadmap or shipping it.

The role is not bounded by the manager's own set of direct reports. As DAI scales and elevates aggressively, the manager brings team-wide and enterprise-wide thinking to the role and steps in to drive cross-cutting initiatives as priorities dictate. The ideal candidate can speak fluently and confidently about the team's work to both internal and external audiences.
People Leadership & Talent

• 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.

Technical Fluency & Coaching

• 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.

Team Health & Enablement

• 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.

Team-Wide Leadership & Enterprise Mindset

• 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.

Partnership & Communication

• 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.

Qualifications

Education

• 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.
• 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

• 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.
• Experience in scientific, chemical, pharmaceutical, or materials-science domains is desired.

Leadership & Competencies

• 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.