Data Scientist II (Soccer) at Teamworks

Job 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. I'm Jacob Mortensen, Senior Manager, Data Science at Teamworks. Our team primarily focuses on building models using both player tracking data and event data to identify undervalued players and characterize the skills that make them effective. The work supports high-stakes recruitment decisions for professional clubs across global football. The team is managing growing demand from partner clubs alongside model maintenance and new development. This hire will take ownership across the model lifecycle, from design and validation through deployment and improvement. That added capacity will help us move important initiatives forward while keeping our existing models accurate and reliable. THE ROLE - 3+ years of professional or research experience developing statistical or machine learning models in Python or R, with examples of work you personally owned from problem formulation and data preparation through validation and use - Strong applied statistical and modeling judgment: you can select appropriate methods, explain their assumptions and trade-offs, and distinguish meaningful predictive signal from artifacts in the data - Experience designing rigorous model evaluations, including preventing data leakage, choosing validation strategies appropriate to temporal or grouped data, quantifying uncertainty, and assessing performance across relevant player populations and contexts - Experience building models with sports tracking data, including working with spatial and temporal relationships, noisy or incomplete observations, and the data-quality challenges that affect model reliability - Ability to write maintainable, tested, and reproducible analytical code, using version control and code review to support collaborative development - Proficiency in SQL to join, transform, and validate large, structured datasets used in model development - Experience taking models beyond exploratory notebooks into repeatable workflows or production systems, collaborating with engineering and investigating issues as models are used - Ability to explain modeling decisions, limitations, and results clearly to technical teammates and professional soccer clubs WHAT I'M LOOKING FOR WHAT YOU MUST BRING - 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 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 WHY THIS ROLE Soccer analytics is moving quickly, and this team is helping shape how advanced modeling is used in recruitment. You'll have meaningful ownership, room to develop new approaches, and the opportunity to see your work used by professional clubs. You'll also help define how the team builds, maintains, and scales its model portfolio as demand grows. This role is not eligible for sponsorship at this time. Please submit your application through this posting only. We're unable to review applications or resumes sent via email, LinkedIn, or other channels. To all recruitment agencies: Teamworks does not accept agency resumes. Please do not forward resumes to our jobs alias, Teamworks employees or any other organization location. Teamworks is not responsible for any fees related to unsolicited resumes. ABOUT TEAMWORKS We're the Operating System for Sports™, powering 7,000+ organizations worldwide, from collegiate programs to every major pro league. Founded in 2006, we've evolved from a messaging tool for college football into the leading sports tech platform, with 600+ global teammates building the future of sports tech. Our solutions span Personnel, Coaching, Performance, Operations, and Intelligence - helping teams recruit smarter, train better, stay compliant, and win. Teamworks is an equal opportunity employer - if you live our core values every day and are honest, hardworking, humble, committed, innovative, and an all-around exceptional person, you'll thrive at Teamworks. We are committed to building a diverse and inclusive workforce and take affirmative action to not discriminate based on race, religion, color, national origin, ancestry, physical disability, mental disability, medical condition, genetic information, marital status, sex, gender, gender identity, gender expression, age, sexual orientation, veteran or military status, or any other legally protected characteristics. This policy applies to all employment practices within our organization, including but not limited to recruiting, hiring, promotion, termination, compensation, benefits, and training. Teamworks is committed to providing reasonable accommodations for candidates with disabilities who need assistance during the hiring process. To request a reasonable accommodation, please email talent@teamworks.com.

Requirements

Must-haves

Strong applied statistical and modeling judgment: you can select appropriate methods, explain their assumptions and trade-offs, and distinguish meaningful predictive signal from artifacts in the data
Experience building models with sports tracking data, including working with spatial and temporal relationships, noisy or incomplete observations, and the data-quality challenges that affect model reliability
Ability to write maintainable, tested, and reproducible analytical code, using version control and code review to support collaborative development
Proficiency in SQL to join, transform, and validate large, structured datasets used in model development
Experience taking models beyond exploratory notebooks into repeatable workflows or production systems, collaborating with engineering and investigating issues as models are used
Ability to explain modeling decisions, limitations, and results clearly to technical teammates and professional soccer clubs
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
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
Please submit your application through this posting only. We're unable to review applications or resumes sent via email, LinkedIn, or other channels.

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

machine learningpythonsqlawsai

Platform: ashby
Effort: quick