Note: The job is a remote job and is open to candidates in USA. Swish Analytics is a sports analytics startup focused on predictive sports analytics data products. They are seeking a Data Scientist to analyze factor usage data and develop machine learning models to enhance their algorithms in sports analytics.
Responsibilities
- Analyze our factor usage data — how factors are used, their downstream effects, and their impact on simulation outputs — and turn that analysis into actionable insight
- Design and set up tests to detect unexpected changes to our sims resulting from factor moves
- Ideate, develop, and improve machine learning and statistical models that drive Swish's core algorithms, growing into factor-optimization modeling as we expand to new sports
- Develop contextualized feature sets that draw on sports-specific domain knowledge
- Contribute across all stages of model development — from proof-of-concept and beta testing to partnering with data engineering and product teams to deploy new models
- Constantly improve model performance using insights from rigorous offline and online experimentation
- Assess model performance, identify weaknesses, and use those findings to direct development efforts
- Document your work and present it clearly to technical and non-technical partners
Skills
- Bachelor's in Data Science, Statistics, Computer Science, Applied Math, or a related technical field; Master's strongly preferred
- 4+ years developing and delivering effective machine learning and/or statistical models to serve real business needs in sports analytics or sports betting
- Strong foundation in Probability Theory, Machine Learning, Inferential Statistics, Bayesian Statistics, and Markov Chain Monte Carlo methods
- Excellent analytical and problem-solving ability, and a demonstrated drive to learn quickly in unfamiliar territory
- Experience with Python and relational SQL
- Experience with source control (GitHub) and related CI/CD processes
- Experience working in AWS environments
- Ability to partner across teams on complex, ambiguous problems and communicate clearly with technical and non-technical audiences
Company Overview