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

Job Role :- Data Science Manager

Location :- Rayleigh , NC (Hybrid-2 Days Onsite)

Duration :- 6 Months C2H

Job Description

• 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.

Qualifications

• 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.