Carefull is an AI-powered financial safety platform that helps banks, credit unions, and wealth advisors protect older-adult customers from fraud and money mistakes. We help financial institutions maintain whole-family relationships while protecting their clients. Carefull’s technology addresses senior-specific financial safety challenges: our monitoring detects fraud patterns missed by industry-standard tools, and our features — identity-theft protection, password and document management, communication tools, and how-to content — help customers maintain financial independence while enabling loved ones to step in when needed.
We are looking for a Data Scientist / AI Engineer to join our Data team to build, evaluate, and improve the AI-powered detection systems at the core of our product. You will work on systems that analyze financial transactions and decide whether to alert a family about potential concerns. This is a hands-on role: you’ll research fraud patterns, design detection logic, write production code, and rigorously evaluate system performance. You will own features end-to-end: from problem understanding to implementation, deployment, and measurement.
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, real-world financial transaction data.
Research fraud and scam typologies relevant to older adults and translate findings into scalable detection logic.
Build evaluation frameworks (metrics, error analysis, model comparisons) to measure system performance and drive improvement.
Optimize AI pipelines for accuracy, latency, and cost, making informed tradeoffs on model selection and architecture.
Collaborate with Customer Service, Go-to-Market, and partner-facing teams to ensure solutions meet real-world needs and deliver measurable impact.
Stay current with developments in LLMs, agent architectures, and applied AI, and identify practical applications for our domain.
Required
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 (time series, inconsistent categorical labeling, incomplete records).
Comfortable reasoning about ambiguity and building systems that handle context-dependent answers.
Clear written and verbal communication in English; able to document reasoning and explain technical decisions to non-technical stakeholders.
Strong Plus
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 data, merchant categorization).
Familiarity with cost optimization for LLM-based systems at scale.
Nice to Have
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).
Silver Screening interview
Take-home challenge
Client technical interview
CTO interview
Final interview Hiring Manager
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