S
Silvervia Ashby
Carefull - Data Scientist / AI Engineer
Argentina$60K - $78K/yrPosted 3w ago
Data ScientistMid LevelFull-time
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About the Role
CAREFULL http://getcareful.com
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.
THE ROLE
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.
WHAT YOU’LL 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, 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.
WHO YOU ARE
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).
INTERVIEW PROCESS
- Silver Screening interview
- Take-home challenge
- Client technical interview
- CTO interview
- Final interview Hiring Manager
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.
THE ROLE
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.
WHAT YOU’LL 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, 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.
WHO YOU ARE
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).
INTERVIEW PROCESS
- Silver Screening interview
- Take-home challenge
- Client technical interview
- CTO interview
- Final interview Hiring Manager
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