Data Scientist (NBA)

Company Description 

Swish Analytics is a sports analytics, betting and fantasy startup building the next generation of predictive sports analytics data products. We believe that oddsmaking is a challenge rooted in engineering, mathematics, and sports betting expertise; not intuition. We're looking for team-oriented individuals with an authentic passion for accurate and predictive real-time data who can execute in a fast-paced, creative, and continually-evolving environment without sacrificing technical excellence. Our challenges are unique, so we hope you are comfortable in uncharted territory and passionate about building systems to support products across a variety of industries and consumer/enterprise clients.  

Job Description

Swish Analytics is looking for a Data Scientist to join our ever-growing team! Data Science is at the core of our business, so this team has true ownership and impact over developing core components of Swish's data products.  We're hiring a Data Scientist to support our NBA and college basketball models.  

Duties:

• Ideate, develop and improve machine learning and statistical models that drive Swish’s core algorithms for producing state-of-the-art sports betting products.
• Develop contextualized feature sets using sports specific domain knowledge.
• Contribute to all stages of model development, from creating proof-of-concepts and beta testing, to partnering with data engineering and product teams to deploy new models.
• Strive to constantly improve model performance using insights from rigorous offline and online experimentation.
• Analyze results and outputs to assess model performance and identify model weaknesses for directing development efforts.
• Adhere to software engineering best practices and contribute to shared code repositories.
• Document modeling work and present to stakeholders and other technical and non-technical partners.

Requirements:

• Bachelor/Master degree in Data Analytics, Data Science, Computer Science or related technical subject area 
• Experience developing models for production scale with a high preference for NBA, college or professional basketball.
• Knowledge in Probability Theory, Machine Learning, Inferential Statistics, Bayesian Statistics, Markov Chain Monte Carlo methods.
• 1-3+ years of demonstrated experience developing and delivering effective machine learning and/or statistical models to serve business needs.
• Experience with relational SQL and Python.
• Experience with source control tools such as GitHub and related CI/CD processes.
• Experience working in AWS environments etc.
• Proven track record of strong leadership skills. Has shown ability to partner with teams in solving complex problems by taking a broad perspective to identify innovative solutions
• Excellent communication skills to both technical and non-technical audiences.