Data Scientist I

GumGum is an advertising technology company transforming advertising through its AI-driven data engine. The Data Scientist I role involves supporting statistical analyses and developing Machine Learning and AI solutions to enhance ad relevance and performance across various platforms.

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 and DL models
• Conduct exploratory data analysis to inform feature development and modeling approaches
• Build, run, and maintain regular pipelines to analyze production data, generate KPIs, and prepare automatic retraining of existing models
• Query, clean, and structure large datasets using SQL, Spark, and cloud data platforms
• Train, evaluate, and iterate on traditional ML models and multimodal deep learning models under guidance from senior team members
• Design and maintain Looker dashboards and other Business Intelligence (BI) tools to track Key Performance Indicators (KPI) for key stakeholders
• Develop and deploy agentic pipelines and other LLM-powered applications, including prompt engineering, tool use, and evaluation of model outputs
• Contribute to existing Machine Learning Engineering (MLE) workflows for model training, deployment, and monitoring
• Document analyses, models, and broader learning to support knowledge sharing across the team and non-technical audiences
• Continuously expand on statistical and AI foundations while learning new AI/ML techniques, tools, and advertising-domain concepts

Skills

• Bachelor's degree in a quantitative field (e.g., Statistics, CS, Math, Physics, or Economics)
• 1–2+ years in a data-driven role such as Analytics, Data Science, or ML Engineering
• Proficiency in Python and experience applying ML/DL methods using libraries like scikit-learn, PyTorch, HuggingFace, or OpenCV
• Dependable SQL skills and experience designing pipelines or DAGs using tools like Airflow or Astronomer
• Exposure to cloud environments (AWS/GCP, Databricks, Snowflake) and large-scale query tools like Spark or Snowpark
• Strong grasp of A/B testing, experimental design, and statistical concepts (regression, classification, optimization)
• Experience with LLM prompting and interest in frameworks like RAG, LangChain, or agentic systems
• Ability to design dashboards for diverse audiences and collaborate effectively with Product and Engineering teams

Benefits

• Employer-matched 401(k) retirement plan
• Participation in a bonus, commission, or stock incentive program

Company Overview

Company H1B Sponsorship