Description

You will build and deploy causal inference models and A/B test frameworks to drive business decisions from complex data analysis.

This role is remote.

Responsibilities

• Develop and implement causal inference models, including Difference-in-Differences (DiD) and Propensity Score Matching (PSM), to assess intervention impact.
• Design and conduct A/B tests to evaluate marketing strategies and product changes.
• Perform statistical analysis on complex datasets to derive actionable insights.
• Collaborate with cross-functional teams to translate business requirements into data-driven solutions.
• Communicate findings and recommendations to stakeholders via clear reports and presentations.

Required Skills

• 5+ years of proven experience developing and applying causal inference models (DiD, PSM).
• Strong background in designing and analyzing A/B tests.
• Proficiency in statistical analysis and data manipulation using Python, PySpark, SQL, and Databricks.
• Strong problem-solving ability with capacity for independent and collaborative work.
• Strong written and verbal communication skills for non-technical audiences.
• Graduate degree in Statistics, Data Science, Economics, or related field.

Preferred Skills

• Previous experience in the retail or pharmacy industry.
• Familiarity with large datasets and data warehousing solutions.

Key Skills

Education

Any Graduate