Product Engineer

San Francisco, California, United States

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Not SpecifiedCompensation

Junior (1 to 2 years)Experience Level

Full TimeJob Type

UnknownVisa

Sports Analytics, Data Science, Betting & GamblingIndustries

Requirements

Candidates should possess a Bachelor's degree in Computer Science, Applied Math, Data Analytics, Data Science, or a related technical subject area, with a Master's degree highly preferred. They must have at least 5 years of demonstrated experience developing and delivering effective machine learning and/or statistical products to meet business needs, along with knowledge in Probability Theory, Machine Learning, Inferential Statistics, Bayesian Statistics, and Markov Chain Monte Carlo methods. Advanced proficiency in Python and SQL is required, as well as experience with source control tools such as GitHub and related CI/CD processes, and experience working in AWS environments. Proven leadership skills and excellent communication skills are also necessary.

Responsibilities

The Product Engineer will expand utilization and adoption of existing models, accelerate adoption of proprietary frameworks, establish and refine KPI's and OKR's for scaling product offerings, apply large-scale data processing techniques to develop scalable sports betting products, proactively improve the Rust codebase, source origins of data inaccuracies, build, test, debug, and deploy production-grade components, keep up to date with new approaches to inferential statistics, examine the integration and scaling of real-world operations, simulations, experiments, and demonstrations, provide risk management guidance, and adhere to software engineering best practices while contributing to shared code repositories.

Skills

Data processing

Python

Rust

Statistical analysis

Experimental design

Large-scale data pipelines

Model deployment

Debugging

Scalable system development

Swish Analytics

Sports analytics and optimization tools provider

About Swish Analytics

Swish Analytics specializes in sports analytics and optimization tools for daily fantasy sports and sports betting, focusing on major U.S. leagues like the NFL, MLB, NBA, and NHL. The company uses an advanced machine learning system to analyze large datasets, providing accurate sports predictions and optimized lineups. This helps users, including individual bettors and professional operators, make informed decisions about their bets and fantasy picks. Swish Analytics differentiates itself by being an Authorized MLB Data Distributor, establishing trust in the sports betting community. Operating on a subscription-based model, users can access various levels of tools and analytics, starting with a free trial. The goal of Swish Analytics is to maximize return on investment for clients by identifying the best bets and balancing risk and reward for long-term success.

San Francisco, CaliforniaHeadquarters

2014Year Founded

$6.5MTotal Funding

EARLY_VCCompany Stage

Fintech, AI & Machine Learning, Financial ServicesIndustries

51-200Employees

Benefits

Remote Work Options

Risks

Increased competition from AI-driven startups could erode Swish Analytics' market share.

Consumer privacy concerns may impact Swish Analytics' data collection practices.

Potential regulation of sports betting advertising could affect Swish Analytics' revenue streams.

Differentiation

Swish Analytics uses proprietary algorithms for accurate sports predictions and optimized lineups.

The company is an Authorized MLB Data Distributor, enhancing its credibility in sports betting.

Swish Analytics offers a subscription model with free trials, attracting diverse user segments.

Upsides

Increased legalization of sports betting in the U.S. expands Swish Analytics' market opportunities.

The rise of AI-driven personalized betting experiences aligns with Swish Analytics' machine learning expertise.

Growing interest in micro-betting offers Swish Analytics a chance to expand its offerings.

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