Data Scientist II – Basketball/Hockey

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Teamworks

SiteLinkedInTodas as Vagas

501 - 1000 funcionários

Fundada em 2005

⚽ Esportes

☁️ SaaS

🤖 Inteligência Artificial

💰 $235.000.000 Series F - Teamworks em 2025-06

Sports • SaaS • Artificial Intelligence

A Teamworks é um SaaS "Sistema Operacional para Esportes" que unifica comunicação, gestão de elenco e pessoal, agendamento, desempenho, treinamento, conformidade, inventário, nutrição e outras operações para equipes profissionais, universitárias, olímpicas/NGB e militares/táticas. A plataforma centraliza fluxos de trabalho, compartilhamento de arquivos, processos automatizados e análises impulsionadas por inteligência artificial (Teamworks Intelligence) para informar aquisição de talentos, construção de elenco, avaliação de atletas e estratégia de jogo. A Teamworks atende milhares de equipes e centenas de departamentos universitários e organizações profissionais em todo o mundo, ajudando as organizações a coordenar funcionários e atletas, otimizar operações e melhorar o desempenho.

Descrição

• Build and transform new data sources into tables, features, and structures that are easy for the team and our clients to build on • Develop, extend, and validate models — including event-probability models and athleticism models — ensuring data representation supports both current and future use cases • Build metrics and analyses that NHL and NBA clients rely on to make decisions, and support client-facing work by digging into the data to answer their questions directly • Extract meaningful features from high-dimensional tracking and pose data, and update existing models to incorporate new signals as they become available • Validate models and outputs — your own and others' — with enough rigor that the team can trust what ships • Write clear reports that communicate technical work to the product team and broader organization

🎯 Requisitos

• 3+ years of experience working with sports tracking data including the kinds of models typically built and the data challenges that come with them • Strong data science fundamentals: you understand how models work, what they need from the data, and how to set data up to support them • Proficiency in Python or R, with solid statistical foundations and comfort with SQL for building and querying structured data • Attention to detail in how data is structured and represented, with an eye for edge cases, consistency, and how downstream users will interact with what you build • A team-first mentality — both teams are small, and being someone others can rely on matters as much as technical skill • For Hockey: direct experience with hockey data or hockey analytics (inside a team, public work, or academically), and familiarity with pose or skeleton data or other high-dimensional spatiotemporal data sources • For Basketball: experience working inside an NBA front office or comparable environment, familiarity with deep learning methods, and public-facing basketball analytics work that demonstrates how you think about the game.

🏖️ Benefícios

• Offers Equity • Offers Bonus