Data Scientist – Payments Risk Analytics

📋 Description

• Evaluate the performance of end-to-end ACH payment decision-making, including ML risk scores and risk rules, focusing on both approvals and losses.

• Identify and assess emerging trends in customer behavior, payment results, and risk indicators.

• Analyze the effects of modifications in strategies, data, or rules on downstream decision-making and outcomes over time.

• Independently formulate analytical inquiries, compile datasets from various sources, and work towards insights without relying on predefined reporting templates.

• Utilize Python for exploratory data analysis, feature evaluation, cohort analysis, and experimentation across extensive ACH and risk datasets.

• Create repeatable analytical workflows and lightweight tools in Python to expedite insight generation and minimize manual analysis efforts.

• Conduct ad hoc analyses to assess the value and applicability of new or existing data signals for risk decision-making.

• Design analyses and reports that facilitate ongoing risk assessments, strategy discussions, and portfolio-level monitoring.

• Investigate and implement both established and innovative analysis techniques to enhance insight generation, trend detection, and decision support within regulated risk and payment datasets.

• Collaborate with Risk, Product, and Relationship Management teams to guide strategy refinement and prioritization.

• Clearly communicate trends, findings, and recommendations to internal stakeholders and, when appropriate, external clients.

• Work with large, imperfect, and regulated datasets to derive actionable conclusions despite challenges such as data gaps, latency, or attribution issues.

⛳️ Requirements

• Over 3 years of experience in payments risk, fraud analytics, or decision-making performance analysis within fintech, payments, or e-commerce sectors.

• Preference for candidates with experience working with ACH or bank transfer payment data.

• Proficient in SQL, with experience in handling large transactional datasets.

• Strong Python skills for data analysis (e.g., pandas, notebooks), experimentation, and analytical automation.

• Experience managing and analyzing data related to risk-based decision systems or similar decision-making frameworks.

• Ability to convert complex analytical results into clear, actionable recommendations for both technical and non-technical audiences.

• Comfortable working in ambiguous situations with incomplete, delayed, or imperfect data.

• Experience utilizing AI-assisted tools or models for data analysis, pattern discovery, or insight generation is an advantage.

🏝️ Benefits

• Competitive salary and performance-based bonuses.

• Comprehensive health insurance package.

• Opportunities for professional development and career advancement.

• Flexible work arrangements to support work-life balance.

• Access to cutting-edge tools and technologies.