Swish Analytics

Data Engineer

• Architect low-latency, real-time analytics systems including raw data collection, feature development and endpoint production
• Build new sports betting data products and predictions offerings
• Integrate large and complex real-time datasets into new consumer and enterprise products
• Develop production-level predictive analytics into enterprise-grade APIs
• Support production systems and help triage issues during live sporting events
• Contribute to the design and implementation of new, fully-automated sports data delivery frameworks

Customer Success Manager

• Establish yourself as an expert and communicator of Swish’s products and services
• Manage day-to-day inbound client inquiries over Slack, email and video conferencing
• Identify customer pain points and proactively reach out with solutions for clients
• Communicate relevant inquiries and tasks with Swish Trading, Product, Engineering and Data Science teams.

Customer Success Associate

Data Scientist

Swish Analytics is a sports analytics, betting and fantasy startup building the next generation of predictive sports analytics data products.
Swish Analytics is hiring a Tennis Data Scientist to join our ever-growing team! Data Science is at the core of our business, so this team has true ownership and impact over developing core components of Swish's data products.
Data Science to support our Sports Data Models
Ideate, develop and improve machine learning and statistical models that drive Swish’s core algorithms for producing state-of-the-art sports betting products.
Develop contextualized feature sets using sports specific domain knowledge.
Contribute to all stages of model development, from creating proof-of-concepts and beta testing, to partnering with data engineering and product teams to deploy new models.
Strive to constantly improve model performance using insights from rigorous offline and online experimentation.
Analyze results and outputs to assess model performance and identify model weaknesses for directing development efforts.
Adhere to software engineering best practices and contribute to shared code repositories.
Document modeling work and present to stakeholders and other technical and non-technical partners.

Data Engineer

Customer Success Associate

Data Scientist, Tennis