[Hiring] Data Scientist II @Teamworks

Role Description

We're hiring a Data Scientist II on our Soccer team to develop the statistical and machine learning models our partner clubs use for player recruitment. This hire will take ownership across the model lifecycle, from design and validation through deployment and improvement.

• Develop statistical and machine learning models that help professional soccer clubs identify undervalued players and strong recruitment targets.
• Use player tracking data to build spatio-temporal models that quantify player impact and skill.
• Build metrics and analyses that soccer teams rely on to make decisions, and support client-facing work by building tools to support model interpretation and understanding.
• 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 clients.
• Partner with our engineering team to deploy models on cloud infrastructure and turn strong analytical work into reliable production systems.
• Review code from other data scientists, share constructive feedback, and contribute ideas that advance the team's work.

Qualifications

• 3+ years of professional or research experience writing code in R or Python for data science, statistics, or machine learning work.
• Comfort with SQL for building and querying structured data.
• Strong data science fundamentals: you understand how models work, what they need from the data, and how to set data up to support them.
• Experience working with sports tracking data, including understanding the kinds of models typically built and the data challenges that come with them.
• Strong communication skills — you'll be explaining modeling decisions directly to partner clubs, not just internal teammates.

Requirements

• Even Better If you have direct experience with soccer data or soccer analytics (inside a team, public work, or academically).
• You have a demonstrated track record of tackling hard problems through published research, blog posts, conference talks, or public projects.
• Experience using a cloud compute provider (e.g. AWS or Google Cloud) to train and deploy models.
• Comfort using AI coding tools, paired with the judgment to recognize when their output needs to be questioned or corrected.

Benefits

• Meaningful ownership and room to develop new approaches.
• Opportunity to see your work used by professional clubs.
• Help define how the team builds, maintains, and scales its model portfolio as demand grows.