Head of Advanced Analytics

Brooklyn Sports & Entertainment•New York, NY

12h•$175,000 - $215,000•Onsite

About The Position

Brooklyn Sports & Entertainment (BSE) is seeking a Head of Advanced Analytics to lead its Data Science, Business Intelligence, and Data Engineering functions. This role will be instrumental in translating the organization's data and analytics strategy into scalable technical capabilities, actionable insights, and measurable business outcomes across BSE's diverse portfolio, including the Brooklyn Nets, New York Liberty, and Barclays Center. The Head of Advanced Analytics will be responsible for advancing a future-ready analytics ecosystem by strengthening enterprise data architecture, building trusted semantic data layers, expanding self-service analytics capabilities, and embedding AI/ML into business processes. The position involves overseeing the design, development, and implementation of advanced analytical models, ensuring data infrastructure reliability, scalability, and governance. This leader will establish technical standards, drive operational excellence, and manage a multidisciplinary team. A key partner to the VP, Data, Analytics, and Insights, this role will collaborate with business leaders to leverage data for improved customer engagement, revenue growth, business optimization, and strategic decision-making.

Requirements

8+ years of progressive experience in data science, advanced analytics, business intelligence, data engineering, or related disciplines, with demonstrated success delivering data-driven solutions that improve business performance.
3+ years of experience managing and developing technical teams, with the ability to lead multidisciplinary functions and balance strategic priorities with hands-on execution.
Bachelor's degree in Data Science, Computer Science, Statistics, Mathematics, Economics, Business, or a related field, or an equivalent combination of education and relevant professional experience.
Hands-on experience with modern data and analytics platforms such as Snowflake, dbt, Tableau, ThoughtSpot, or comparable technologies, including the development of trusted data models, scalable reporting, and self-service analytics capabilities.
Strong technical proficiency in SQL, Python, R, database management, data visualization, and cloud-based data infrastructure.
Experience designing and implementing modern data architectures, including semantic data layers, data transformation frameworks, automated pipelines, and data governance practices.
Demonstrated experience developing, deploying, and maintaining predictive and machine learning models, from initial research and proof of concept through production implementation and ongoing optimization.
Strong understanding of artificial intelligence, machine learning, statistical modeling, experimentation, and emerging analytics technologies, with the ability to identify practical applications that deliver business value.
Experience working within cloud environments, preferably Amazon Web Services, and familiarity with modern data engineering and machine learning infrastructure.
Proven ability to manage complex technical projects, prioritize competing business needs, and deliver scalable solutions in a collaborative, cross-functional environment.
Strong communication and presentation skills, with the ability to translate complex technical concepts into clear business recommendations for senior leadership and nontechnical stakeholders.
Experience working in Agile development environments and implementing processes that improve team effectiveness, solution quality, and delivery.

Nice To Haves

Experience applying advanced analytics within sports, entertainment, media, hospitality, ticketing, or other consumer-focused industries.
Experience developing customer-focused models, including lifetime value, propensity, retention, segmentation, and marketing effectiveness.
Familiarity with medallion architecture, machine learning operations, enterprise data governance, and responsible AI practices.
Amazon Web Services certification or comparable cloud platform credentials.

Responsibilities

Lead Advanced Analytics Functions: Oversee the day-to-day strategy, execution, and performance of the Data Science, Business Intelligence, and Data Engineering teams, ensuring alignment with organizational priorities, consistent technical standards, and delivery of measurable business value.
Advance the Enterprise Data Ecosystem: Lead the design and implementation of scalable data infrastructure, including a medallion architecture framework that organizes raw, refined, and business-ready data into trusted layers to improve data quality, governance, lineage, accessibility, and performance.
Build Trusted Semantic Data Layers: Develop and maintain semantic data models that translate complex underlying data structures into consistent business definitions, standardized metrics, and reusable datasets, enabling reliable self-service analytics across the organization.
Transform Business Intelligence and Visualization: Establish scalable dashboarding standards, intuitive data access tools, visualization guidelines, and reporting governance practices that enable stakeholders to quickly find, interpret, and act on trusted business insights.
Champion AI-Driven Analytics: Identify and implement opportunities to integrate artificial intelligence, natural-language analytics, intelligent recommendations, automation, and guided insights into the tools and workflows employees use to access information and make decisions.
Lead Data Science and Machine Learning Development: Oversee the design, development, testing, deployment, and monitoring of descriptive, predictive, and prescriptive analytical models. Establish best practices for model accuracy, reliability, performance, validation, and governance.
Drive Fan and Customer Growth Initiatives: Lead the development of advanced analytical solutions supporting customer lifetime value forecasting, subscriber lapse propensity, lead scoring, season-ticket member retention, customer segmentation, media mix modeling, and other initiatives designed to improve acquisition, engagement, retention, and revenue.
Strengthen Data Engineering and Integration: Direct the development and optimization of sustainable data pipelines, automated workflows, and integration processes that support enterprise reporting, marketing activation, sales effectiveness, and advanced analytical applications.
Build and Develop High-Performing Teams: Manage, mentor, and develop a multidisciplinary team of data scientists, business intelligence professionals, and data engineers. Establish clear performance expectations, foster technical excellence, and create opportunities for professional growth and collaboration.
Establish Analytical Standards and Best Practices: Define and implement consistent methodologies for data analysis, experimentation, analytics instrumentation, machine learning development, and data governance to ensure analytical rigor, operational efficiency, and continuous improvement.
Partner with Business Leaders: Collaborate with stakeholders across Marketing, Ticketing, Corporate Partnerships, Digital, Finance, Hospitality, and Operations to understand business challenges, prioritize analytical initiatives, and translate complex data into actionable recommendations that support organizational objectives.
Drive Data Adoption and Business Impact: Promote data literacy and self-service analytics across the organization, identify gaps in data accessibility and utilization, and establish KPIs to measure the effectiveness, adoption, and business impact of analytics solutions.