Swish Analytics

Swish Analytics

Data Science Manager (MLS) - Swish Analytics (San Francisco · CA · Open to Remote)

Swish Analytics
Jobs in San Francisco · CA · Open to Remote
Marketing: Database Marketing/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 enterprise clients. This position is 100% Remote

Swish Analytics is looking for a hands-on Data Science Manager - Major League Soccer 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.

Duties:

  • Provide hands-on leadership to data professionals to execute on the following deliverables:
    • Own the end-to-end data pipeline that will be used for developing predictive models
    • Develop algorithms to provide player props for in-game play for MLS
    • Communicate and track key performance metrics for model
    • Mentor others on the team while owning independent deliverables
  • Ideate, develop and improve machine learning and statistical models that drive Swish’s core algorithms for producing state-of-the-art sports betting products
  • 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
  • Analyze results and outputs to assess model performance and identify model weaknesses for directing development efforts.
  • Document modeling work and present to stakeholders and other technical and non-technical partners.

Requirements:

  • Masters degree in Data Analytics, Data Science, Computer Science or related technical subject area; PhD degree preferred
  • Demonstrated experience developing models at production scale for MLS, sportsbook, or other professional soccer leagues
  • Proficient in Probability Theory, Machine Learning, Inferential Statistics, Bayesian Statistics, Markov Chain Monte Carlo methods
  • 5+ years of demonstrated experience developing and delivering effective machine learning and/or statistical models to serve business needs
  • Demonstrated experience leading a team of 3 or more Data Scientists
  • Advanced level skills in Python to ingest, clean, model and visualize data
  • Understanding of the sports betting marketplace or experience analyzing sports data
  • Proven ability to train/mentor team members in utilizing best practices for software development as well as data science implementations
  • Experience with source control tools such as GitHub and related CI/CD processes
  • Experience working in AWS environments etc
  • 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: $170,000 - 210,000

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.