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# Data Scientist (Causality)

- **Company:** Wizeline, Inc.
- **Location:** United States (Remote available)
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Big Data, Data Mining, Distributed Systems, R (Programming Language), Python (Programming Language), Machine Learning, SQL Databases, Sql Optimization, Pyspark, Scikit Learn, Information Technology, Statistics Packages
- **Published:** July 14, 2026
- **Apply:** https://arc.dev/remote-jobs/j/redirect/p4j7j3x1a8

## About the Role

* 5-7 years of experience in causal inference, experimentation (RCTs), and machine learning.
 * Bachelor's degree in Statistics, Economics, Computer Science, or related field.
 * English level: B2.
 * Strong foundations in statistics, probability, and applied econometrics (selection bias, omitted variable bias, spillovers, SUTVA).
 * Advanced proficiency in Python (econml, causalml, dowhy, statsmodels, scikit-learn) and R (did, fixest, CausalImpact, MatchIt, Synth, gsynth).
 * Advanced SQL and PySpark for data extraction, transformation, and analysis.

Nice-to-have

 * AI Tooling Proficiency: Ability to leverage AI tools for drafting, analysis, research, or process automation, and recommend effective AI use in workflows.
 * Experience operating in dynamic, high-ambiguity environments.

Soft Skills

 * Strategic and persuasive communication with business orientation.
 * Strong storytelling to explain and defend complex solutions.
 * Ability to lead conversations with clients and senior stakeholders.
 * Clear, structured responses to objections or ambiguous scenarios.
 * Skill in translating technical topics into business impact.
 * Building trust and credibility through communication.

## Description

* Causal Modeling: Design, build, and validate causal models to measure the impact of business initiatives (Difference-in-Differences, Synthetic Control, Potential Outcomes framework).
 * Experimentation: Implement randomized controlled trials (RCTs), ensuring robust design, group assignment, and statistical validity.
 * Statistical Analysis: Apply advanced statistical methods to distinguish correlation from causation, evaluate assumptions, and quantify causal effects.
 * Data Tools: Use Python, R, SQL, and PySpark to analyze large-scale datasets in distributed environments.
 * Documentation & Reporting: Maintain clear documentation of models and experiments, and translate complex findings into actionable insights for non-technical stakeholders.


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