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# Data Scientist

- **Company:** RECRUITER EXCHANGE INC
- **Location:** United States (Remote available)
- **Experience:** Expert
- **Salary:** $170,000.0 - $210,000.0
- **Contract:** Permanent contract
- **Skills:** Data Analysis, Fraud Prevention and Detection, Python (Programming Language), SQL Databases, Tableau (Software), Apache Spark, Build Management, Data Analytics
- **Published:** November 7, 2026
- **Apply:** https://arc.dev/remote-jobs/j/redirect/ps071l5r2c

## About the Role

* At least seven years in data science, quantitative modeling, or a data-focused role such as product or business analytics, with demonstrated impact in fraud or risk.
 * Strong practical experience with Python/R and SQL.
 * Expertise in BI platforms such as Tableau, Sigma, or Metabase.
 * The ability to structure and analyze complex datasets using exploratory data analysis and cohort analysis, and explain findings to technical and nontechnical audiences, including clients.
 * Strong critical thinking and creative problem-solving skills, with a proactive approach to execution.
 * Proficiency in defining, tracking, and communicating performance metrics.
 * A self-motivated approach, ownership of outcomes, and a strong orientation toward growth.

Preferred Expertise

 * Experience with Spark.

Remote Work and Compensation

## Description

* Build and deploy fraud prevention models for fintech use cases, taking work from initial prototypes to scalable production solutions.
 * Define and monitor metrics that measure risk product performance and the effectiveness of risk management strategies.
 * Analyze complex datasets to identify opportunities to reduce fraud and improve client approval rates.
 * Work directly with clients to understand fraud challenges and turn analytical findings into clear, actionable recommendations.
 * Use data and models to develop risk mitigation strategies and interventions while preserving and improving the user experience.
 * Create and automate self-service dashboards with BI tools.
 * Partner with engineering to scale models, improve production performance, and support data instrumentation.
 * Translate requirements from Business, Product, and Engineering into data-driven solutions.