Data Scientist - Research and Development

The Pirates Why

The Pittsburgh Pirates are a storied franchise in Major League Baseball who are reinventing themselves on every level. Boldly and relentlessly pursuing excellence by:

• purposefully developing a player and people-centered culture;
• deeply connecting with our fans, partners, and colleagues;
• passionately creating lifetime memories for generations of families and friends; and
• meaningfully impacting our communities and the game of baseball.

At the Pirates, we believe in the power of a diverse workforce and strive to create an inclusive culture centered in Passion, Innovation, Respect, Accountability, Teamwork, Empathy, and Service.

Job Summary

As a Data Scientist on the Pirates Research & Development team, you will help transform a wealth of baseball data — from box scores and player tracking to video and biomechanics — into actionable insights that drive the Pirates to make better, faster acquisition, development, and deployment decisions. You will work closely with other data scientists, analysts, and software engineers across Baseball R&D as well as other stakeholders across Baseball Operations (scouts, coaches, player development, front office) to turn your statistical and machine learning models into actionable decision tools.

Responsibilities:

1. Design, build, validate, and deploy statistical and/or machine-learning models to support all facets of baseball operations, including scouting, player acquisition, player development, and on-field decision making.
2. Build tools, prototypes, and visualizations to translate complex data and model results into insights understandable by coaches, players, and decision-makers.
3. Communicate results and insights clearly to both technical and non-technical audiences.
4. Partner with data engineers to build scalable data pipelines and maintain data quality.
5. Stay abreast of new data sources, analytical techniques, and research.
6. Help the organization experiment, learn, and iterate.

Qualifications

We recognize that no candidate will meet every qualification listed below. If you are excited about this role and believe you can add value to our work, we encourage you to apply even if your experience does not align perfectly with every requirement.

Required:

1. Degree (or equivalent experience) in a quantitative discipline (e.g., Statistics, Computer Science, Mathematics, Economics, Machine Learning, Biomechanics, Engineering, Operations Research).
2. Demonstrated experience applying complex statistical and/or machine learning tools to real-world problems.
3. Demonstrated proficiency in a programming language such as Python or R for data analysis and modeling.
4. Demonstrated ability to communicate complex quantitative concepts clearly, both written and verbally.
5. Demonstrated experience collaborating with others on data science projects.
6. Authorized to work lawfully in the United States.

Desired:

1. Familiarity with advanced statistical techniques (e.g., fixed-effect / random-effect models, generalized additive models, Bayesian modeling, probabilistic programming).
2. Experience with machine-learning / deep-learning frameworks (e.g., PyTorch, Tensorflow), especially applied to high-dimensional, spatiotemporal, or biomechanical data.
3. Background in computer vision, biomechanics, sports-science, or modeling of dynamic physical systems.
4. Prior experience in sports analytics context; baseball is a plus.
5. Experience with database languages (e.g., SQL) and working with large / relational datasets.

Equal Opportunity Employer

The Pittsburgh Pirates are an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability status, protected veteran status or any other characteristic protected by law.

We recognize that no candidate will meet every qualification listed below. If you are excited about this role and believe you can add value to our work, we encourage you to apply even if your experience does not align perfectly with every requirement.

Familiarity with advanced statistical techniques (e.g., fixed-effect / random-effect models, generalized additive models, Bayesian modeling, probabilistic programming).
Experience with machine-learning / deep-learning frameworks (e.g., PyTorch, Tensorflow), especially applied to high-dimensional, spatiotemporal, or biomechanical data.
Background in computer vision, biomechanics, sports-science, or modeling of dynamic physical systems.
Prior experience in sports analytics context; baseball is a plus.
Experience with database languages (e.g., SQL) and working with large / relational datasets.