The company is looking to solve problems related to media analytics, product innovation, and applied machine learning within the advertising technology environment. They need to translate data into actionable insights for client-facing analytics projects and assist with model configuration and deployment.
Requirements
- Foundational knowledge of statistics, machine learning concepts, and experimentation principles.
- Proficiency in Python and SQL for data analysis and modeling
- familiarity with Databricks, AWS Sagemaker, or other cloud compute platforms is a plus.
- Coursework or projects involving causal inference, A/B testing, or media measurement.
- Exposure to data tools such as Pandas, Scikit-learn, or Jupyter notebooks.
Responsibilities
- Support the design and analysis of experiments including lift studies, A/B tests, and causal inference methods.
- Assist with model evaluation, performance monitoring, and diagnosing model drift.
- Contribute to audience modeling, segmentation, and scoring projects under senior DS guidance.
- Gain exposure to how data science products are deployed and measured in client environments.
- Learn to build and adapt configurations for existing modeling products (e.g., audience scoring, bid modifiers).
- Assist with data pipeline tasks, sampling logic, and feature transformations.
- Shadow engineers and product managers to understand how prototypes become production tools.
Other
- Currently pursuing or recently completed a Bachelor's or Master's degree in Data Science, Statistics, Computer Science, Economics, or a related quantitative field.
- Strong communication skills with the ability to explain technical concepts in simple terms.
- Curiosity, adaptability, and a willingness to learn in ambiguous situations.
- Interest in digital advertising, media, or marketing technology.
- Familiarity with advertising concepts (impressions, CTR, CPM) or digital campaign workflows.
