Product Engineer

Summary

Join our team as a Product Engineer at Swish Analytics! We're looking for a science-driven individual to work on creating enhancements for future generation products, scaling internal tools, and setting the stage for making big bets on new products and features.

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
• 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