Tennis Data Scientist - Swish Analytics

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 Tennis Data Scientists 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. This position is remote from the USA.

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:

Masters degree in Data Analytics, Data Science, Computer Science or related technical subject area

Demonstrated experience developing models at production scale for Tennis or sports betting for 2+ years

Expertise in Probability Theory, Machine Learning, Inferential Statistics, Bayesian Statistics, Markov Chain Monte Carlo methods

5+ years of demonstrated experience developing and delivering effective machine learning and/or statistical models to serve business needs in sports or sports betting

Experience with relational SQL & 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