Senior Data Scientist

The New York Jets are building a world-class football analytics program that turns complex data into clear, actionable decisions. As a Senior Data Scientist, you’ll lead high-impact modeling and research across player evaluation, coaching support, and football strategy—while setting the technical standard for how we design, validate, deploy, and communicate models s role is for a collaborative, high-ownership teammate who can architect end-to-end pipelines and evaluation processes, and who is fluent in modern deep learning within a competitive environment.

• Architect modeling systems that are repeatable, auditable, and production-ready: data → features → training → evaluation → delivery/monitoring.
  • Design evaluation frameworks (calibration, uncertainty estimation, bias/error analysis) to ensure models are ready to support high impact decisions.
  • Deliver research and analysis for:
    • Player evaluation (e.g., forecasting, role/fit analysis, contextualization across situations).
    • Coaching support requests (rapid-turn insights with clear assumptions/limitations).
    • Football research (longer-horizon studies that improve the organization’s decision-making).
  • Partner cross-functionally with coaching, scouting, performance, and football operations to define problems, translate questions into measurable outcomes, and communicate results clearly.
  • Mentor and raise the bar for DS best practices: code quality, review, documentation, experimentation standards, and reproducibility.

Required Qualifications:

• Football / Domain – Prior NFL or sports experience is preferred (club, league, or NFL-adjacent role where you shipped analytics that informed football decisions).
• Strong understanding of football, with the ability to translate football questions into rigorous analyses without over-complicating the output.
• Proven ability to collaborate in high-trust environments with diverse stakeholders and tight timelines.

Technical Leadership & Modeling Architecture – Demonstrated experience owning end-to-end modeling pipelines—including data sourcing, feature design, training, evaluation, deployment/packaging, and ongoing monitoring.

• Expertise in building evaluation processes beyond a single model:
• Principled baselines.
• Back testing and leakage prevention.
• Uncertainty quantification and calibration.
• Clear decision recommendations and tradeoffs.
• Ability to operate at “Principal” level: scoping ambiguous problems, setting technical direction, and aligning stakeholders.

Deep Learning – Hands-on experience applying deep learning in production or near-production settings, including one or more of the following:

• Neural nets and/or transformers
• Representation learning / embeddings
• Modern training practices (regularization, optimization, early stopping, hyperparameter search)
• Proficiency with at least one deep learning framework (e.g., PyTorch or Tensor Flow) and comfort working with GPU-enabled workflows

Data & Software Foundations – Strong proficiency with Python or R and SQL.

• Strong software engineering habits:
• Version control (Git), code review, testing.
• Modular, maintainable codebases.
• Documentation and reproducibility (experiment tracking, model/version provenance).
• Experience integrating data from multiple sources and producing reliable datasets for downstream modeling and reporting.

Communication & Teamwork – Excellent written and verbal communication—able to explain methods, results, and limitations to both technical and non-technical partners.

• Demonstrated ability to deliver both:
• Rapid-turn answers (triage, directional insight).
• Deep research (rigorous studies with clear takeaways).
• Low-ego teammate: collaborative, pragmatic, and committed to shared success.

Preferred Qualifications:

• Master’s degree or higher in a quantitative field (e.g., Statistics, CS, Applied Math, Physics, Engineering, Economics).
• Experience with one or more of:
• Causal inference.
• Bayesian/hierarchical modeling.
• Time-series or survival modeling
• MLOps tooling (containers, orchestration, CI/CD patterns, model registries).
• Databricks or similar orchestration platforms.
• Experience leading and mentoring other data scientists and influencing technical standards across a team.

Sala…