Data Scientist I

GumGum•Santa Monica, CA

2d•$128,000 - $130,000•Hybrid

About The Position

The Data Scientist I supports the development and deployment of Machine Learning (ML), statistical analysis, and Artificial Intelligence (AI) solutions that improve the relevance and value of ads across our Ad Exchange, Contextual Platform, and Attention Measurement Platform. This role focuses on applying strong analytical foundations, building and evaluating ML models, and partnering closely with the Engineering, Product, and Data Science team to improve ad-serving performance and operational decision-making. The ideal candidate is curious, data-driven, and eager to develop a strong understanding of the advertising domain and the systems that power large-scale decision-making. You will work with large datasets, contribute to production ML systems, and gain hands-on experience across the ML lifecycle — from exploration to monitoring. Note: GumGum fosters a flexible work environment, offering GumGummers the ability to work either in-office or remotely/from home. For this position, in-person/office collaboration is required 2 days per week, supporting a balanced approach to flexibility and team engagement.

Requirements

Bachelor’s degree in Statistics, Mathematics, Physics, Economics, Computer Science or a related quantitative or technical field
1–2+ years in a data-driven role (Analytics, Data Science, or similar) -- WE ARE OPEN TO RECENT GRADS WITH INTERNSHIP EXPERIENCE
Experience applying ML and statistical methods to real-world datasets
Experience collaborating with Engineering and/or Product teams

Nice To Haves

exposure to production ML systems or MLOps tools (MLFlow, Weights & Biases, etc,)
familiarity with advertising, marketplaces, or large-scale decision systems

Responsibilities

Support the translation of business and product requirements into data-driven analyses and ML solutions
Partner with Engineering team members and senior Data Scientists to develop, test, and deploy ML models
Conduct exploratory data analysis to inform feature development and modeling approaches
Query, clean, and structure large datasets using SQL, Spark, and cloud data platforms
Train, evaluate, and iterate on ML models under guidance from senior team members
Contribute to existing MLOps workflows for model training, deployment, and monitoring
Help define and track Key Performance Indicators in our Business Intelligence tools to measure model performance and downstream impact
Document analyses, models, and learnings to support knowledge sharing across the team
Continuously learn new ML techniques, tools, and advertising-domain concepts