Data Scientist

Role Overview

Data Scientist (Senior Risk Strategy Analyst)

• Location: Remote (US-based)
• Duration: 12-month contract + extensions
• Pay: $47/hr

You will build and optimize fraud and credit risk strategies for a fast-growing fintech organization, using data, analytics, and automation to identify emerging risks, improve customer outcomes, and support business growth.

Responsibilities

• Develop, maintain, and optimize fraud and credit risk strategies to meet business performance objectives.
• Analyze large datasets using SQL and Python alongside statistical and data science methods to uncover trends, risks, and opportunities.
• Design and refine automated monitoring rules, risk controls, and decision strategies across the customer lifecycle.
• Build dashboards and reporting/monitoring tools using Tableau (or other BI platforms) to deliver actionable insights.
• Partner cross-functionally with Product, Engineering, Operations, and Risk to define requirements and execute initiatives.
• Detect emerging fraud patterns and risk exposures; recommend data-driven process improvements.
• Lead complex investigations and analytical projects to support business decisions.
• Mentor junior analysts and help establish team best practices.

Requirements

• 5+ years of experience in Fraud Strategy, Credit Risk Strategy, Risk Analytics, or related roles in fintech/e-commerce/online payments/lending/financial services.
• Advanced SQL and Python with experience on large-scale datasets.
• Strong knowledge of statistical analysis, risk modeling, and data science methodologies.
• Experience building data-driven business rules, monitoring frameworks, and automated risk controls.
• Proven ability to solve complex business problems and deliver measurable outcomes.
• Strong communication skills with the ability to present to both technical and non-technical stakeholders.
• Experience building dashboards using Tableau (or similar BI tools).

Preferred Qualifications

• Experimental design, A/B testing, and data governance.
• Knowledge of fraud typologies (e.g., onboarding fraud, synthetic identity fraud, account takeover, abuse prevention).
• Experience with customer management/collections/portfolio strategies for credit products.
• Familiarity with machine learning for fraud detection and risk management.