As data scientist for BOXRAW Labs, you will work on pushing the envelope of Boxing through predictive analytics. You will work closely with a core R&D team on key projects for the long term vision of transforming our understanding of boxing.
The ideal candidate is a practiced engineer with the ability and desire to provide leadership and imagine superior architectures. They must be self-directed and comfortable managing the needs of a variety of stakeholders and team members.
This is a high visibility, high impact position to change the landscape of boxing.
Specifically, data Scientists discover the information hidden in vast amounts of data, and make smarter decisions to deliver even better fighters. The focus is on doing statistical analysis on sports data, and building high-quality prediction systems integrated with our products. Automate scoring using machine learning techniques, build recommendation systems, build systems for boxing match detection.
Responsibilities and Duties
- Data manipulation: Be comfortable in querying large datasets, with the ability to help manipulate and construct the right data structures starting with the end in mind. With an eye for data quality to ensure rigor of input data going into models.
- Maintenance: Have a good blend of data science with data engineering concepts in order to help monitor and maintenance of model production pipelines, including implementation of error handling and testing of analytics code.
- Stakeholder management: Being able to communicate with different levels of the business and gather their needs
- Storytelling: Ability to translate data science concepts into business language and create a compelling storyline from it
- Data visualization: Work with others to design, deploy and maintain online tools
- Data experimentation: Eager to learn and/or challenge existing forecast models and apply cutting edge machine learning techniques
Skills & Requirements
- A degree in Computer Science, Physics, Mathematics or a similar quantitative subject
- A solid understanding of statistics (hypothesis testing, regressions, random variables, inference)
- Comfortable with presenting back to technical and non-technical stakeholders through effective data visualization and building of reporting frameworks
- Knowledge of sports analytics or applied data science techniques to sports
- Experience accessing and combining data from multiple sources and building data pipelines, including a good knowledge of SQL
- Comfortable working in a Python data science tech stack (e.g. pandas, NumPy, sci-kit-learn, PySpark, PyMC3, Dash, Plotly)
- The ability to work collaboratively and proactively in a fast-paced environment alongside both scientists, engineers, and non-technical stakeholders
- A ‘hackers’ mentality, comfortable using open-source technologies.
- Basic knowledge of software development lifecycles, engineering, and machine learning practices (Data pipelines, API workflows, CI/CD deployments, DataOps, MLOps)