Junior Quantitative Analyst

LOS ANGELES DODGERS LLCLos Angeles, CA
7d$85,000 - $95,000Onsite

About The Position

The Los Angeles Dodgers are looking for early-career quantitative baseball researchers who are passionate about baseball and excited to apply data science to player evaluation, development, and strategy. You will work with experienced researchers to design and implement statistical and machine learning models that provide impact on and off the field. We are especially interested in candidates with either (1) experience in deep learning with applications to spatiotemporal data or (2) experience in Bayesian hierarchical modeling & probabilistic forecasting.

Requirements

  • Bachelor’s or Master’s degree in a quantitative field or equivalent experience
  • Demonstrated experience applying statistics and machine learning to research problems
  • Strong communication skills
  • Passion for baseball
  • Proficiency in Python, SQL, and version control

Nice To Haves

  • Exposure to deep learning frameworks (Jax/PyTorch/TensorFlow)
  • Experience working with time-series, image, or video data
  • Coursework or projects involving Bayesian statistics and probabilistic programming (NumPyro, PyMC, or Stan)
  • Familiarity with forecasting, uncertainty quantification, or hierarchical modeling
  • Understanding of physics and biomechanics
  • Experience with open-source contributions or academic research
  • Experience with agentic coding tools
  • Experience with data analysis in sports, robotics, health wearables, autonomous systems, or aerospace.
  • Baseball research experience is a plus.

Responsibilities

  • Assist in the development, refinement, and maintenance of models of baseball, integrating new data sources and methods where appropriate
  • Collaborate across the organization to identify and answer important research questions
  • Write clear, reliable code for data analysis and model implementation
  • Learn, implement, and develop best practices for model development
  • Participate in internal seminars and code reviews to grow technical and baseball knowledge
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