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NFL Data Scientist

Swish Analytics · Posted 240 days ago

Remote San Francisco INTJINTP EngineeringDataSales
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About the Role

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 an NFL Data Scientists 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. This position is remote from the USA.

Duties:

  • 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 techni

Personality Fit

This role is commonly a great fit for these MBTI types:

Job ID: b3f074c2-ae68-441e-acca-2b6ae522d4f9

Posted via Remote OK

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