Data Scientist (Causal Inference / Marketing Measurement)

Job Title: Data Scientist (Causal Inference / Marketing Measurement)

Location: Onsite Menlo Park, CA

Duration: 12 Months

We are looking for a highly skilled Data Scientist V with deep expertise in causal inference, Difference‑in‑Differences (DiD) methodologies, and Python‑based modeling. This role sits at the forefront of marketing measurement research, where you will design, test, and optimize state‑of‑the‑art methods to quantify marketing impact with greater accuracy, lower variance, and improved performance. You will work closely with experts in marketing science, experimentation, and data modeling to advance how marketing effectiveness is measured at scale

Responsibilities

• Build and optimize causal inference models for marketing impact measurement.
• Develop and validate Difference‑in‑Differences (DiD) and other quasi‑experimental methods.
• Conduct back‑testing using historical marketing campaign data.
• Compare new methodologies against existing production models.
• Improve model accuracy, reduce variance, and enhance performance.
• Collaborate with marketing science and analytics teams to refine measurement frameworks.
• Document methodology and ensure reproducibility in Python workflows

Required:

• Advanced Python proficiency, including statistical and causal modeling libraries.
• Hands‑on experience with causal inference models, especially for impact measurement.
• Strong expertise in Difference‑in‑Differences (DiD) methodologies and their implementation in Python.
• Solid understanding of econometrics, quasi‑experimental methods, and statistical inference.
• Experience validating models through back‑testing, variance analysis, and robustness checks.
• Ability to translate methodological concepts into scalable, production‑ready code.

Preferred (Nice to Have)

• Experience with survey data analysis.
• Familiarity with AI/ML workflows.
• Background in marketing analytics, incrementality measurement, or media performance analysis.