[Hiring] Data Scientist / AI Engineer @Silver.dev

Role Description

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.

Qualifications

• 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.

Requirements

• 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 with Hiring Manager