Product Engineer (MLOps)

Position Overview

• Location Type: 100% Remote
• Employment Type: Full-time
• Salary: (Salary not provided in the original text)

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 enterprise clients.

The Data Science team is hiring an experienced Machine Learning Engineer with a background building machine learning and statistical modeling frameworks from scratch. They can assist with optimizing the different aspects of the modeling process (Data Validation, Data Visualization, Data Stores & Structures, Feature Engineering, Model Training & Evaluation, Deployments) and improving a variety of Swish products. They will know when to “roll your own” and when to outsource a particular step in the modeling process. They will engineer custom solutions to solve complex data-related sports challenges across multiple leagues.

Requirements

• Masters degree in Computer Science, Applied Mathematics, Data Science, Computational Physics/Chemistry or related technical subject area
• 5+ years of demonstrated experience developing and delivering clean and efficient production code to serve business needs
• Demonstrated experience developing data science modeling systems and infrastructure at scale
• Experience with Python and exposure to modern machine learning frameworks

Responsibilities

• Design, prototype, implement, evaluate, optimize systems to generate sports datasets and predictions with high accuracy and low latency.
• Evaluate internal modeling frameworks and tools to optimize data scientist's modeling workflow.
• Build, test, deploy and maintain production systems.
• Work closely with DevOps and Data Engineering teams to assist with implementation, optimization and scale workloads on Kubernetes using CI/CD, automation tools and scripting languages.
• Support maintenance and optimization of cloud-native EDW and ETL solutions.
• Maintain and promote best practices for software development, including deployment process, documentation, and coding standards.
• Experience applying large scale data processing techniques to develop scalable and innovative sports betting products.
• Use extensive experience to build, test, debug, and deploy production-grade components.
• Participate in development of database structures that fit into the overall architecture of Swish systems.

Application Instructions

• (Application instructions not provided in the original text)

Company Information

• Company: Swish Analytics
• Industry: Sports Analytics, Betting, Fantasy
• Description: A sports analytics, betting and fantasy startup building the next generation of predictive sports analytics data products.