The Role
We are seeking a highly skilled Senior Data Scientist to join our Measurement & Outcomes team. Reporting into the Data Science organization, you will work closely with our Outcomes Tech Lead as a key technical partner and core contributor to build and scale our methodology.
This is a high-impact role where your work will directly shape how major brands and publishers measure the effectiveness of their advertising investments across linear and digital environments.
What You'll Do
- Support the design, development, and deployment of advanced statistical models to optimize advertising campaign measurement and improve planning and targeting accuracy.
- Develop and refine methodologies for core outcome products, specifically attribution and lift models.
- Adapt existing measurement models to solve for specific client use cases.
- Ensure the integrity and accuracy of data through rigorous testing and validation of models.
- Work closely with Engineering and MLOps teams to integrate models into production systems, ensuring scalability and reliability.
- Communicate complex analytical concepts and results to stakeholders in a clear and concise manner.
What You'll Bring
- Master's or Ph.D. in Mathematics, Statistics, Computer Science, Data Science, or a related field.
- 2–5+ years of proven experience in data science, with a strong focus on statistical modeling and machine learning.
- Proficiency in programming languages such as Python and SQL.
- Expertise in statistical techniques including regression analysis, hypothesis testing, A/B testing, causal inference, Bayesian methods, and time-series analysis.
- Experience with general machine learning frameworks and libraries.
- Strong analytical and problem-solving skills, with the ability to work with complex datasets and extract actionable insights.
- Excellent communication and presentation skills, with the ability to convey technical information to non-technical audiences.
- Proficiency with AI tools such as Gemini and Claude.
Bonus Points
- Familiarity with big data technologies such as Snowflake, Spark, Databricks, and cloud platforms (AWS, GCP, etc.).