Causal Inference Data Scientist - Experiments & Metrics

DRISHTICON Inc; United States; On-site; USD 120,000 - 160,000; Full time

Job summary

DRISHTICON Inc. seeks a Data Scientist skilled in statistical inference and causal analysis to design measurement frameworks for enterprise solutions. You will experiment, quantify impact, and build scalable measurement systems.

You will design and run experiments (A/B tests, propensity score methods), collaborate with stakeholders to define KPIs, and write clean, reproducible code with CI/CD practices in a cloud-enabled environment.

Qualifications

• Strong knowledge of hypothesis testing, OLS, GLM, and causal inference techniques.
• Proficiency in Python and SQL with libraries such as statsmodels, scikit-learn, DoWhy, linearmodels.
• Experience with A/B testing and experimental design.
• Familiarity with Databricks or similar enterprise cloud environments.
• Self-starter with an ownership mindset and ability to work independently.

Responsibilities

• Design and implement measurement frameworks for solutions in production.
• Apply statistical inference and causal methods (e.g., A/B testing, propensity score matching, instrumental variables).
• Develop and analyze controlled experiments and observational studies.
• Collaborate with stakeholders to define KPIs and measurement strategies.
• Write clean, reproducible code for statistical analysis and reporting.
• Implement CI/CD principles and manage code repositories using GitHub Enterprise.

Skills

Statistical inference

Causal analysis

Experiment design

A/B testing

Python

SQL

DoWhy

linearmodels

CI/CD principles

Ownership mindset

Tools

Databricks

GitHub Enterprise

statsmodels

scikit-learn

DoWhy

linearmodels

Job description

DRISHTICON Inc. seeks a Data Scientist skilled in statistical inference and causal analysis to design measurement frameworks for enterprise solutions. You will experiment, quantify impact, and build scalable measurement systems.

You will design and run experiments (A/B tests, propensity score methods), collaborate with stakeholders to define KPIs, and write clean, reproducible code with CI/CD practices in a cloud-enabled environment.