Data Scientist

This role begins as a contract position at an hourly rate of $200 USD per hour, providing a streamlined path toward a permanent, salaried full-time transition. Please note that while the contract phase offers a higher hourly rate in lieu of benefits, the full-time conversion introduces a comprehensive total rewards package, including premium health coverage and retirement programs, alongside a restructured annual salary.

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.

Projects may span:

• Predictive and propensity modeling (fan churn, lookalike audiences, conversion likelihood, lifetime value)
• Marketing performance, attribution, and media mix modeling
• Pricing, demand forecasting, and inventory optimization for ticketing and hospitality
• Recommendation and personalization systems for fan-facing experiences
• AI agent prototypes for sales, service, content, and internal workflows
• Evaluation frameworks for LLM and agentic systems (accuracy, latency, cost, safety, drift)
• Custom analytics and insight tooling for Next League consultants and client stakeholders

The following and other duties may be assigned as necessary:

• 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

To perform this job successfully, an individual must be able to perform each essential duty satisfactorily. The requirements listed below are representative of the knowledge, skill, and ability required. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.

• 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

• 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

• Data scientists who want to do more than build models, and who are excited to ship applied AI that drives client outcomes
• Builders who are equally comfortable in a Jupyter notebook and in a client meeting
• Pragmatists who can move from messy data to a working pilot quickly without losing analytical rigor
• Practitioners excited to apply data science and AI across sports, fans, and live experiences

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