[Remote] Machine Learning Engineer at Swish Analytics

San Francisco, California, United States

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Not SpecifiedCompensation

Mid-level (3 to 4 years), Senior (5 to 8 years)Experience Level

Full TimeJob Type

UnknownVisa

Sports Analytics, Betting, Fantasy SportsIndustries

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
A proven background in quantitative analytics, trading, or engineering
Demonstrated experience developing data science modeling systems and infrastructure at scale
Experience with Python and exposure to modern machine learning frameworks
Proficient in SQL; experience with MySQL
Background and/or interest in Rust preferred
Affinity for teamwork and collaboration with others to solve problems, share knowledge, and provide feedback
Strong communication skills when discussing technical concepts with technical and non-technical colleagues

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

Skills

Machine Learning

Statistical Modeling

Feature Engineering

Model Training

Model Evaluation

Kubernetes

CI/CD

ETL

Data Validation

Data Visualization

Python

Cloud

DevOps

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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