Data Scientist / AI Engineer (Fintech)

TL;DR

Data Scientist / AI Engineer (Fintech): Build, evaluate, and improve AI-powered detection systems that analyze financial transactions for fraud patterns targeting older adults with an accent on data enrichment pipelines, LLM workflows, and scalable detection logic. Focus on researching scam typologies, designing evaluation frameworks, and optimizing pipelines for accuracy, latency, and cost.

Location: Remote (Argentina)

Compensation: $60K – $78K • Offers Equity • Yearly offsites

Company

hirify.global is an AI-powered financial safety platform that helps banks, credit unions, and wealth advisors protect older-adult customers from fraud and money mistakes.

What you will do

• Own end-to-end implementation of AI-driven detection features from discovery to production deployment and iteration.
• Design and build data enrichment pipelines to extract structured information from messy financial transaction data.
• Research fraud and scam typologies relevant to older adults and translate into scalable detection logic.
• Build evaluation frameworks including metrics, error analysis, and model comparisons to measure and improve performance.
• Optimize AI pipelines for accuracy, latency, and cost with tradeoffs on model selection and architecture.
• Collaborate with Customer Service, Go-to-Market, and partner teams to ensure solutions deliver real-world impact.

Requirements

• Clear written and verbal communication in English; able to document reasoning and explain to non-technical stakeholders.
• Strong Python skills with experience building data pipelines and production systems.
• Hands-on experience with LLMs in production: designing workflows, handling structured outputs, managing context, and evaluating performance.
• Experience with evaluation methodology: precision/recall tradeoffs, confusion matrices, error analysis, statistical significance.
• Ability to work with messy tabular data including time series, inconsistent labeling, and incomplete records.
• Comfortable reasoning about ambiguity and building context-dependent systems.

Nice to have

• Experience with LangChain, LangGraph, or similar agent orchestration frameworks.
• AWS experience (Lambda, CDK, Bedrock, Redshift, DynamoDB).
• Background in fraud detection, financial services, or risk/compliance.
• Experience with financial transaction data (ACH, Zelle, wire transfers, POS, merchant categorization).
• Familiarity with cost optimization for LLM-based systems at scale.
• Experience working with regulated industries or bank partners.
• Exposure to elder care, aging-in-place, or financial vulnerability research.
• Background in data science or ML beyond LLMs (statistical modeling, anomaly detection).

Hiring process

• Silver Screening interview
• Take-home challenge
• Client technical interview
• CTO interview
• Final interview Hiring Manager