Qualifications

• 3+ years of applied Data Science experience.
• Strong experience with causal inference, causal ML, econometrics, or experimentation.
• Proficient in measuring treatment effects and incremental business impact.
• Hands-on experience with statistical methods like Difference-in-Differences, Matching, CATE, Panel Data Analysis, Uplift Modeling, and Heterogeneous Treatment Effects.
• Strong Python and SQL programming skills.
• Familiarity with Git for version control.
• Proven track record of developing production-quality ML or analytics solutions.
• Bachelor's or Master's in a relevant quantitative field.

Responsibilities

• Design and implement causal inference and machine learning solutions.
• Measure business treatment impact on customer behavior, revenue, and retention.
• Apply various statistical methods for analysis, including Difference-in-Differences and Uplift Modeling.
• Define and manage experimental frameworks, metrics, and evaluation timelines.
• Build production-ready ML pipelines adhering to software engineering best practices.
• Collaborate with business teams to turn challenges into analytical solutions.
• Develop Generative AI solutions, including prompt engineering and LLM workflows.
• Evaluate and integrate emerging AI/ML technologies for production use.
• Communicate technical findings to both technical and non-technical audiences.
• Mentor team members through technical guidance and code reviews.

Benefits

• Opportunity to work at the forefront of AI and machine learning technologies.
• Collaboration with multidisciplinary teams across the organization.
• Exposure to real-world business problems and the ability to directly measure their impact.
• Continued professional development and mentorship opportunities.
• Access to cutting-edge tools and platforms, including Generative AI applications.