Hiring Data Science Manager || Location : Raleigh , NC (Hybrid)

Client is seeking a hands-on Manager, Data Science to lead a high-impact team focused on an Agentic Content Platform. This is a player-coach role combining people leadership, technical strategy, and selective hands-on Data Science contribution. The manager will lead development of shared agents, evaluation capabilities, and platform foundations while owning delivery of assigned content streams end to end. The ideal candidate is a strong technical leader with deep expertise in Generative AI, LLMs, RAG, agentic systems, and large-scale structured and unstructured data, while also having the leadership experience necessary to build and develop a high-performing Data Science team. Responsibilities Scope & Impact Set the vision and strategic priorities for AI across the content platform. Act as a recognized Data Science expert and technical leader. Own delivery of assigned content streams, including quality, timeliness, and automation. Lead and develop a team of Data Scientists. Drive applied research with a clear path to production. Build and scale evaluation science capabilities, including offline evaluation frameworks, automated benchmarking, and human-in-the-loop feedback systems. Champion rapid prototyping and iterative development. Collaborate with other Data Science teams to maximize reuse of components and patterns. Coordinate across multiple cross-functional teams, systems, and domains. Determine where to automate versus where human editors should remain in the loop. Select appropriate tools and technologies based on business requirements. Technical & Product Leadership Define and execute the AI roadmap for the content platform. Prioritize reusable platform capabilities and agent-based workflows over one-off solutions. Translate ambiguous business problems into clear technical strategies and delivery plans. Identify technical tradeoffs and alternative approaches when constraints arise. Design and oversee production-grade AI systems focused on accuracy, reliability, scalability, and appropriate human oversight Partner with Product, Engineering, and Architecture leaders to establish shared AI foundations. Replace bespoke tooling with reusable workflows and capabilities. Lead by example through hands-on coding, prototyping, experiments, and production model development. Establish Data Science standards for experimentation, evaluation, deployment, monitoring, performance, and reliability. Team & Operational Excellence Build, mentor, and develop a high-performing Data Science team. Establish clear goals, priorities, operating rhythms, and accountability. Foster curiosity, adaptability, responsible innovation, knowledge sharing, and continuous learning. Support individual growth and career development. Foster collaboration across Product, Engineering, Design, Legal, and other business functions. Promote technical excellence and continuous improvement. Ensure the team has the skills, resources, and organizational support needed to deliver against business priorities.

8+ years of relevant experience in Data Science, Machine Learning, or Applied AI. 4+ years of leadership experience, including direct or indirect team management. Bachelor’s degree in a relevant field with significant applied experience in Data Science, ML, or AI. Master’s or PhD in Data Science, Computer Science, Statistics, or related field strongly preferred, or equivalent practical experience. Strong Python proficiency Experience with LLM and ML tooling, including Google ADK, LangChain/LangGraph, TensorFlow, PyTorch, and prompt-tuning techniques. Experience building multi-agent or orchestrated LLM systems, including task decomposition, tool use, routing, state management, and failure handling. Familiarity with vector databases, knowledge graphs, and hybrid retrieval architectures. Strong experience working with structured and unstructured data at scale. Experience designing and implementing data pipelines and data preparation workflows. Experience integrating ML into complex, multi-stage processing systems and event-driven architectures. Working knowledge of containerization, CI/CD, REST APIs, and model-serving tools. Familiarity with LLM observability and evaluation tooling, including tracing, offline evaluation harnesses, LLM-as-judge, and human-review pipelines. Cloud infrastructure experience with AWS, Azure, or GCP, with AWS preferred. Familiarity with AI coding tools such as GitHub Copilot, Claude Code, and OpenAI Codex. Preferred Qualifications Advanced degree in Data Science, Computer Science, Statistics, or a related technical field. Experience building reusable AI platform capabilities rather than isolated AI applications. Deep experience designing evaluation frameworks for LLM and agentic systems. Experience balancing automation with human-in-the-loop workflows in production AI systems.