Data Scientist

Summary

Join Swish Analytics, a sports analytics startup, as an NHL Data Scientist! This remote, US-based position offers the opportunity to significantly impact the development of core data products. You will ideate, develop, and improve machine learning and statistical models for state-of-the-art sports betting products. The role involves collaborating with data engineering and product teams throughout the model development lifecycle, from proof-of-concept to deployment. You will analyze model performance, identify weaknesses, and contribute to software engineering best practices. A competitive base salary is offered, ranging from $107,000 to $175,000.

Requirements

• Hold a Masters degree in Data Analytics, Data Science, Computer Science or related technical subject area
• Demonstrate experience developing models at production scale for NHL or sports betting for 1+ years
• Possess expertise in Probability Theory, Machine Learning, Inferential Statistics, Bayesian Statistics, Markov Chain Monte Carlo methods
• Demonstrate 4+ years of experience developing and delivering effective machine learning and/or statistical models to serve business needs in sports or sports betting
• Have experience with relational SQL & Python
• Have experience with source control tools such as GitHub and related CI/CD processes
• Have experience working in AWS environments
• Possess a proven track record of strong leadership skills and the ability to partner with teams in solving complex problems by taking a broad perspective to identify innovative solutions
• Have excellent communication skills to both technical and non-technical audiences

Responsibilities

• 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

Benefits

Base salary: $107,000-175,000