Remote NFL Data Scientist: Predictive Sports Analytics

Swish Analytics Inc.; San Francisco (CA); On-site; USD 140,000 - 180,000; Full time

Job summary

Swish Analytics is seeking an NFL Data Scientist to join our growing team. Data Science drives our predictive sports analytics platforms, with true ownership over core components of Swish's data products. This position is remote from the USA.

Responsibilities include developing ML and statistical models, creating contextual feature sets, and deploying models with data engineering and product teams. You will push for continual improvement through rigorous offline and online experimentation.

Qualifications

• MSc in Data Analytics, Data Science, CS or related field.
• 2+ years of production-scale modeling for NFL/CFB/sports betting.
• Expertise in Probability Theory, ML, Inferential/Bayesian Statistics, MCMC.
• 5+ years delivering ML/statistical models for sports/sports betting.
• Proficient in SQL and Python; familiar with GitHub and CI/CD.
• Experience in AWS; strong leadership and cross-team collaboration.
• Excellent communication for technical and non-technical audiences.

Responsibilities

• Ideate, develop and improve ML and statistical models for Swish's core algorithms.
• Develop contextualized feature sets using sports domain knowledge.
• Collaborate with data engineering and product teams to deploy models.
• Improve model performance via offline and online experiments.
• Analyze results and identify model weaknesses to direct work.
• Adhere to software engineering best practices and document work.

Skills

Python

SQL

Machine Learning

Statistics

Bayesian Statistics

Probability Theory

Leadership

Communication

AWS

GitHub

Education

Master's degree in Data Analytics/DS/CS or related

Tools

GitHub

Job description

Swish Analytics is seeking an NFL Data Scientist to join our growing team. Data Science drives our predictive sports analytics platforms, with true ownership over core components of Swish's data products. This position is remote from the USA.

Responsibilities include developing ML and statistical models, creating contextual feature sets, and deploying models with data engineering and product teams. You will push for continual improvement through rigorous offline and online experimentation.