Manager, Data Science

Job Title: Manager Of Data Science

Location: Raleigh, NC (27606)

Duration: 6 Months (Contract to Hire)

Role Overview
We are seeking a hands-on Manager of Data Science to lead a high-impact team on our Agentic Content Platform - building the shared agents, evaluation, and platform core capabilities that run our content streams, and owning the delivery of some streams end to end. This is a player-coach role combining people's leadership, technical strategy, and selective hands-on data science contribution.

Key Responsibilities
Scope & Impact

• Set the vision and strategic priorities for AI across the content platform, acting as a recognized expert for Data Science
• Own delivery of your assigned content streams - quality, timeliness, and automation level - while contributing reusable capability back to the shared platform
• Lead and develop a team of data scientists, setting the cultural tone for the group
• Drive applied research with a clear path to production, keeping the business outcome as the first priority and working within real-world constraints such as latency and reliability
• Build and scale evaluation science capabilities within the team, including offline evaluation frameworks, automated benchmarking pipelines, and human-in-the-loop feedback systems to rigorously measure model quality and business impact
• Champion hands-on rapid prototyping and iteration
• Collaborate with other Data Science teams to maximize re-use of components and patterns, eliminating waste, duplication and unnecessary customization
• Operate with broad scope, coordinating across multiple cross-functional teams, systems, and domains
• Exercise judgment about where to automate, where to keep a human editor in the loop, and how to move that line over time
• Select the right tools and technologies for the business problem

Technical & Product Leadership

• Define and execute the AI roadmap for the content platform, prioritizing reusable platform capabilities and agent-based workflows over one-off solutions.
• Translate ambiguous business problems into clear technical strategies and delivery plans, identifying tradeoffs and alternative approaches when constraints arise.
• Design and oversee production-grade AI systems that meet customer requirements for accuracy, reliability, scalability, and appropriate human oversight.
• Partner with Product, Engineering, and Architecture leaders to establish shared foundations, integrate AI into the platform at scale, and replace bespoke tooling with reusable workflows.
• Lead by example through hands-on technical contributions, including writing code, developing and demonstrating prototypes, and contributing to experiments and production models.
• Establish and scale Data Science standards for experimentation, evaluation, deployment, monitoring, performance, and reliability across both the team’s solutions and shared capabilities.

Team & Operational Excellence

• Foster a culture of curiosity, adaptability, responsible innovation, knowledge sharing, and continuous learning, enabling the team to evolve as technologies, customer needs, and business priorities change.
• Build, mentor, and develop a high-performing data science team, supporting individual growth and career development.
• Establish clear goals, priorities, operating rhythms, and accountability for the team’s work.
• Foster effective collaboration across Product, Engineering, Design, Legal, and other business functions.
• Promote a culture of technical excellence, responsible innovation, knowledge sharing, and continuous improvement.
• Ensure the team has the skills, resources, and organizational support needed to deliver against business priorities.

Core Qualifications
Experience & Education

• Advanced degree (Master’s or PhD) in Data Science, Computer Science, Statistics, or a related field strongly preferred, or equivalent practical experience
• Bachelor’s degree in a relevant field with significant applied experience in data science, machine learning, or AI
• Typically requires:
• 8 years of relevant experience in data science, machine learning, or applied AI
• 4 years of leadership experience (direct or indirect team management)

We recognize that exceptional candidates may follow non-traditional paths and value demonstrated impact, technical depth, and leadership over strict credential requirements.
Technical Proficiency

• Proficient with Python, ML and LLM tooling such as Google ADK, LangChain/LangGraph, ML frameworks (e.g. TensorFlow, PyTorch) and prompt tuning techniques
• Experience building multi-agent or orchestrated LLM systems - task decomposition, tool use, routing, state and failure handling
• Familiarity with vector databases, knowledge graphs, and hybrid retrieval architecture
• 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
• Working knowledge of containerization, CI/CD, RESTful API design and model serving tools
• Familiarity with LLM observability and evaluation tooling (tracing, offline eval harnesses, LLM-as-judge and human review pipelines)
• Cloud infrastructure experience on AWS (preferred), Azure, or GCP
• Familiarity with AI coding tools (e.g. GitHub Copilot, Claude Code, OpenAI Codex)