About this role
Location
Starts as remote, and later becomes hybrid in Raleigh, NC (relocation required; candidates able to relocate at the start of the contract are preferred, though relocation may occur upon conversion to full-time).
Experience Level
Senior/Managerial Level (8+ years of relevant data science/ML experience; 4+ years of leadership experience).
Role Overview
We are seeking a hands-on Manager of Data Science to lead a high-impact team building shared agents, evaluation frameworks, and platform core capabilities for an agentic content platform. This is a player-coach role combining people leadership, technical strategy, and selective hands-on data science contribution, with ownership over budgets, forecasting, planning, and resourcing. The ideal candidate has meaningful experience designing, architecting, and implementing an agentic RAG-based system, and can translate complex technical concepts into clear language for business partners.
Key Responsibilities
Scope & Strategic Impact
• Set the vision and strategic priorities for AI across the content platform, acting as a recognized expert for Data Science.
• Own delivery of assigned content streams — quality, timeliness, and automation level — while contributing reusable capability back to the shared platform.
• Drive applied research with a clear path to production, prioritizing business outcomes within real-world constraints such as latency and reliability.
• Build and scale evaluation science capabilities, including offline evaluation frameworks, automated benchmarking pipelines, and human-in-the-loop feedback systems.
• Collaborate with other Data Science teams to maximize reuse of components and eliminate duplication.
Technical & Product Leadership
• Define and execute the AI roadmap for the content platform, prioritizing reusable platform capabilities and agent-based workflows.
• Translate ambiguous business problems into clear technical strategies and delivery plans.
• Design and oversee production-grade AI systems meeting requirements for accuracy, reliability, scalability, and human oversight.
• Partner with Product, Engineering, and Architecture leaders to integrate AI into the platform at scale.
• Lead by example through hands-on technical contributions, including writing code and developing prototypes.
• Establish and scale Data Science standards for experimentation, evaluation, deployment, and monitoring.
Team & Operational Excellence
• Build, mentor, and develop a high-performing data science team, supporting career growth.
• Establish clear goals, priorities, operating rhythms, and accountability for the team's work.
• Foster effective collaboration across Product, Engineering, Design, and other business functions.
• Oversee budgets, forecasting, planning, and resourcing for the team.
• Promote a culture of curiosity, responsible innovation, and continuous learning.
Required Qualifications
• 8+ years of relevant experience in data science, machine learning, or applied AI.
• 4+ years of leadership experience (direct or indirect team management).
• Advanced degree (Master's or PhD) in Data Science, Computer Science, Statistics, or a related field strongly preferred; equivalent practical experience also considered.
• Demonstrated experience designing, architecting, and implementing an agentic RAG-based system, with evidence of meaningful technical decision-making.
• Proficiency with Python and ML/LLM tooling (e.g., LangChain/LangGraph, TensorFlow, PyTorch, prompt tuning techniques).
• Experience building multi-agent or orchestrated LLM systems, including task decomposition, tool use, routing, and failure handling.
• Strong experience working with structured and unstructured data at scale.
• Ability to design and implement data pipelines and preparation workflows.
• Experience integrating ML into complex, multi-stage processing systems, including event-driven architectures.
• Cloud infrastructure experience on AWS, Azure, or GCP.
• Strong communication skills, with the ability to explain complex concepts to business partners in clear, non-technical language.
Preferred Qualifications
• Familiarity with vector databases, knowledge graphs, and hybrid retrieval architecture.
• Working knowledge of containerization, CI/CD, RESTful API design, and model serving tools.
• Familiarity with LLM observability and evaluation tooling, including tracing, offline evaluation harnesses, and LLM-as-judge/human review pipelines.
• Familiarity with AI coding assistants (e.g., GitHub Copilot or similar tools).
Core Skills & Attributes
• Strong player-coach mindset, balancing people leadership with hands-on technical contribution.
• Excellent ability to translate complex technical concepts into clear, jargon-free language for business stakeholders.
• Strong judgment in balancing automation with human oversight in AI system design.
• Proven ability to build and scale high-performing technical teams.
• Collaborative leadership style across cross-functional teams and domains.
• Comfortable operating with broad scope across multiple systems and business priorities.