Product Engineer at Swish Analytics

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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 Product Engineer, to join our ever-growing team! Science is at the core of our business, and our Research & Experimentation branch works to create enhancements for future generation products, scale internal tools as our team and customers continue to grow, and set the stage for making big bets on new products and features. This is a great opportunity to work with thought leaders in the Sports Data space to create new and transformative products.

Duties:

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.

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

Base salary: $133,000-180,000

Swish Analytics is an Equal Opportunity Employer. All candidates who meet the qualifications will be considered without regard to race, color, religion, sex, national origin, age, disability, sexual orientation, pregnancy status, genetic, military, veteran status, marital status, or any other characteristic protected by law. The position responsibilities are not limited to the responsibilities outlined above and are subject to change. At the employer’s discretion, this position may require successful completion of background and reference checks.

Estimated Salary range:

Our salary intelligence, powered by our AI algorithms, has calculated an estimated range for this position between $70,000 - $90,000 . Please note that the actual salary for this position may vary, as it is dependent on various factors including but not limited to experience, location, and market dynamics.

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