Quantitative Analyst

Los Angeles Dodgers Hiring - Quantitative Analyst

Los Angeles Dodgers is hiring a Quantitative Analyst with 0 - 3 years of experience. Based in United States - Los Angeles, CA and with In-office ways of working.

Job description and responsibilities:

The Los Angeles Dodgers are looking for quantitative baseball researchers to turn data into actionable insights through the use of mathematical and statistical models. Analysts build and evaluate models, engineer and orchestrate model deployment, and provide data-driven insights to coaches and front office decision-makers on decisions regarding on-field strategy, player development, and player evaluation.

We are especially interested in candidates with either (1) demonstrated strength in deep learning with applications to spatiotemporal data or (2) demonstrated strength in Bayesian hierarchical modeling & probabilistic forecasting.

Essential Duties/Responsibilities:

• Develop, refine, and maintain models of baseball, integrating new data sources and methods where appropriate
• Collaborate across the organization to identify important research questions and translate model outputs into actionable recommendations for player evaluation, development, and in-game strategy
• Own production code end-to-end: data management, version control, CI/CD, testing, deployment, and monitoring
• Develop internal tools and facilitate code reuse
• Mentor junior analysts and represent the Dodgers at conferences, workshops, and campus events

Requirements and qualifications:

Basic Requirements/Qualifications:

• Experience: 2+ years of experience building and evaluating predictive models in industry or equivalent academic experience
• Core Skills: Proficiency in Python, SQL, version control, and reproducible research practices

Preferred Expertise:

Deep learning track

• You have built and evaluated deep-learning models for tasks such as detection, segmentation, motion forecasting, or anomaly detection in jax, PyTorch, or a similar tensor library
• You have built models using data from spatial sensors
• You have built models that incorporate physics, domain constraints, or graph structure

Bayesian modeling track

• You have built and evaluated Bayesian hierarchical models
• You have implemented Gaussian-process, state-space, or other latent-factor structures for time-series forecasting in domains with sparse or noisy data.
• You have written probabilistic models in NumPyro, PyMC, or Stan with custom likelihoods and priors
• You have applied predictive distributions to decision-making in some domain

Additional signals of impact:

• Understanding of physics and biomechanics
• Experience with agentic coding tools
• Experience deploying ML systems using terabytes of data
• Success applying data analysis in sports, robotics, health wearables, autonomous systems, or aerospace. Baseball research experience is a plus.
• Experience writing queries on large SQL databases, engineering ETL pipelines, and/or working with data lakehouses