Data Science Manager

SMART TECH SKILLS LLC

5d•Hybrid

About The Position

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.

Requirements

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.
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.

Nice To Haves

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).

Responsibilities

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.
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.
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.