Product Engineer

Summary

Join Swish Analytics, a sports analytics startup, as a Product Engineer and contribute to the development of next-generation predictive sports analytics data products. Work with thought leaders in the sports data space to create transformative products. Expand utilization and adoption of existing models and accelerate adoption of commonly used proprietary frameworks. Establish and refine KPIs and OKRs for scaling and accelerating product offerings. Proactively improve the Rust codebase and source origins of data inaccuracies. Build, test, debug, and deploy production-grade components. Keep up-to-date with new approaches to inferential statistics and experimental design. Examine the integration and scaling of real-world operations and simulations.

Requirements

• Bachelor degree in Computer Science, Applied Math, Data Analytics, Data Science or related technical subject area; Master degree highly preferred
• 5+ years of demonstrated experience developing and delivering effective machine learning and/or statistical products to serve business needs
• Knowledge in Probability Theory, Machine Learning, Inferential Statistics, Bayesian Statistics, Markov Chain Monte Carlo methods
• Advanced Python & SQL
• Experience with source control tools such as GitHub and related CI/CD processes
• Experience working in AWS environments
• 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

Responsibilities

• Expand utilization and adoption of existing models and accelerate adoption of commonly used proprietary frameworks
• Establish and refine KPI's and OKR's for scaling and accelerating the product offerings
• Experience applying large scale data processing techniques to develop scalable and innovative sports betting products
• Proactively improve our Rust codebase
• Source origins of data inaccuracies through data pipeline dependencies and Python code base
• Use extensive experience to build, test, debug, and deploy production-grade components
• Keep up to date with new approaches to inferential statistics, sampling, and experimental design
• Examine the integration and scaling of our real world operations, simulations, experiments, and demonstrations
• Expert Python developer using many different machine learning and data science frameworks
• Provide risk management guidance on methods for assessing and mitigating risk
• Skill in developing or recommending analytic approaches or solutions to problems and situations for which information is incomplete or for which no precedent exists
• Adhere to software engineering best practices and contribute to shared code repositories

Benefits

Base salary: $133,000-180,000