Data Scientist

Next League, LLC

1d•Remote

About The Position

Next League is seeking a Data Scientist to join the AI Strategy, Training and Transformation team. This role pairs rigorous applied data science with hands-on AI agent prototyping to power how we deliver measurable outcomes for our sports, entertainment, and league clients. The role sits at the intersection of quantitative modeling and applied AI engineering. You will design and ship models that drive revenue, marketing performance, fan engagement, and operational efficiency, while also prototyping AI agents and applied AI tooling that scale our delivery across client accounts. You will collaborate primarily with product, strategy, and engineering counterparts, with occasional embedding on client engagements to lead technical discovery, audit data, and architect modeling solutions. Clients include professional teams, leagues, major sports properties, and national governing bodies.

Requirements

3 to 5 years of experience in data science, applied machine learning, or quantitative analytics roles
Strong foundation in statistics, machine learning, and experimental design
Proficiency in Python and SQL, with comfort in at least one major ML library (scikit-learn, PyTorch, TensorFlow, XGBoost, or similar)
Demonstrated experience building and deploying models against real business problems with measurable outcomes
Hands-on experience working with large language models and at least one agent or RAG framework
Strong product instincts and the ability to ship pragmatic v1s under tight timelines
Exceptional communication skills, with the ability to translate model outputs into business recommendations
Comfort operating in ambiguity and shaping problems before solving them
Knowledge of responsible AI practices, governance considerations, and enterprise data handling

Nice To Haves

Sports, entertainment, league, media, or performance marketing experience strongly preferred
Hands-on experience with leading AI platforms such as: Claude, ChatGPT / OpenAI APIs, Google Gemini / Vertex AI, Microsoft Copilot (M365 / Copilot Studio), AWS Bedrock, Azure AI Foundry
Familiarity with agentic frameworks (LangChain, LangGraph, OpenAI Agents SDK, CrewAI)
Experience with vector databases (Pinecone, Weaviate, pgvector) and embedding models
Experience with cloud data platforms such as Snowflake, BigQuery, or Databricks
Familiarity with marketing analytics, CRM data (Salesforce, HubSpot, Dynamics 365), ticketing systems, or fan engagement platforms
Experience with automation tools such as n8n or Zapier
Comfort with core system concepts: APIs, permissions, retrieval and search, event tracking
Exposure to MLOps and model deployment workflows

Responsibilities

Design, build, and deploy machine learning and statistical models that address client business challenges across revenue, marketing, and fan engagement
Lead exploratory data analysis, feature engineering, and model selection across structured and unstructured client data
Translate ambiguous business problems into well-scoped modeling initiatives with clear success metrics
Build evaluation frameworks and monitor model performance, drift, and business impact over time
Document modeling decisions, assumptions, and tradeoffs in a way that earns trust with both technical and non-technical stakeholders
Prototype AI agents, copilots, and assistants using leading frameworks such as LangChain, LangGraph, OpenAI Agents SDK, and Claude
Build retrieval-augmented (RAG) pipelines and tool-using agent workflows for internal and client use cases
Develop evaluation rubrics for agent quality covering accuracy, latency, cost, and safety guardrails
Partner with engineering to harden prototypes into production-ready solutions
Stay current on the rapidly evolving model and tooling landscape, and bring practical recommendations back to the team
Support AI Product Managers and consultants with quantitative analysis, modeling, and technical deep-dives
Occasionally embed with client teams to lead data audits, modeling workshops, and technical architecture discussions
Communicate complex modeling results to non-technical stakeholders with clarity and credibility
Contribute to executive-ready deliverables including business cases, demos, and rollout plans
Help shape Next League's internal data science and AI tooling stack
Build repeatable patterns that scale modeling and agent work across the client portfolio, including notebooks, evaluation harnesses, prompt libraries, and modeling templates
Contribute to responsible AI, model governance, privacy, and enterprise data handling standards