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)