Senior Data Scientist

Brooklyn Sports & Entertainment•New York, NY

30d•$100,000 - $160,000•Onsite

About The Position

Brooklyn Sports & Entertainment creates bold, authentic, and unforgettable experiences that redefine sports, entertainment, and hospitality, The Brooklyn Way. As the parent company of marquee properties including the Brooklyn Nets, NY Liberty and Barclays Center, Brooklyn Sports & Entertainment operates at the intersection of live entertainment, premium hospitality, and community engagement. As it expands its existing portfolio, Brooklyn Sports & Entertainment now includes a media portfolio including Type.Set.Brooklyn and BK Mag, as well as Brooklyn Wine Club, and a hospitality business developing several location-based entertainment properties. Whether on the court, on stage, or in the workplace, Brooklyn Sports & Entertainment is driven by a pursuit of excellence—constantly evolving, innovating, and pushing boundaries to elevate its business and expand its fan base. The company is deeply committed to fostering a culture of belonging and inclusion, both internally and across its communities, ensuring that every interaction reflects its values of growth mindset, integrity, accountability and care. We're looking for a Senior Data Scientist to modernize how we build, deploy, and maintain predictive and statistical models. This isn't a role for maintaining what exists; it's for someone who sees what data science should look like in three years and can start building toward it now. You'll blend applied statistical judgment with MLOps operating excellence, and you'll do it with an AI-first approach. You use modern AI tooling to accelerate model development, experimentation, deployment, monitoring, and documentation, while maintaining the rigor and reliability that production systems require. When automation isn't enough, you build. This is a senior individual contributor role with no direct reports. You'll set the standard for how machine learning gets done at BSE and help the broader team ship faster and more confidently.

Requirements

Bachelor’s degree in a quantitative field (Statistics, Computer Science, Economics, Engineering, Mathematics) or equivalent practical experience; advanced degree a plus.
5+ years in applied machine learning, data science, analytics, or related roles with demonstrated ownership of models from development through production impact.
AI-first work style: you habitually use AI to accelerate how you think, build, and communicate—and you can explain your approach.
Solid grounding in statistical inference and modeling (e.g., regression/classification, time series, causal thinking, experiment design/measurement, uncertainty estimation).
Practical experience with MLOps concepts: reproducibility, model versioning, evaluation, deployment patterns, monitoring/alerting, and data quality checks.
Comfort working with modern data ecosystems (warehouses, pipelines, BI consumers), and partnering with engineering to productionize solutions.
Strong SQL and deep comfort working directly in Snowflake to validate logic, troubleshoot discrepancies, and build repeatable analyses a plus.
A fast learner of AI tooling: you enjoy experimenting with new tools, quickly separating signal from noise, and integrating practical improvements into repeatable workflows.
Strong communicator: ability to explain model behavior, tradeoffs, and uncertainty clearly to technical and non-technical stakeholders.

Responsibilities

Own the end-to-end model lifecycle: establish and run the operating model for how we develop, validate, deploy, monitor, and iterate ML and statistical models in production.
Be AI-first in model development: use AI to accelerate feature ideation, experimentation, model prototyping, documentation, and troubleshooting—building repeatable workflows that scale beyond individual projects.
Provide statistical and methodological leadership: advise teams on appropriate techniques (inference vs prediction, experimental vs observational methods, uncertainty, bias/variance tradeoffs), and set standards for model validity and interpretation.
Design practical MLOps patterns: implement lightweight, scalable patterns for versioning, reproducibility, evaluation, CI/CD, and monitoring (data drift, model drift, performance regressions).
Enable faster, safer deployment: partner with engineering and analytics to productionize models and ensure they are reliable, observable, and maintainable with clear ownership and runbooks.
Standardize evaluation and governance: define requirements for testing, backtesting, model cards/notes, documentation, and approval pathways—especially for models that influence decisions.
Stay current and integrate new technology thoughtfully: evaluate emerging tools and practices; separate signal from noise; integrate improvements into the workflow without chasing hype.
Uplevel the team through enablement: create playbooks, templates, and training so analysts and scientists can ship models with consistent quality and speed.