At Silent Eight, we develop our own AI-based products to combat financial crimes that enable things like money laundering, the financing of terrorism, and systemic corruption. We're a leading RegTech firm working with large international financial institutions such as Standard Chartered Bank and HSBC. Join us and help make the world a safer place!
We solve hard, real-world problems — from uncovering financial crime, fraud patterns and mule networks, through prioritizing thousands of alerts, to crafting defendable case narratives. We work close to users (analysts, investigators, risk/compliance), iterate fast, and deliver in weeks, not quarters. The adversary adapts — this is an intelligence game, not an academic benchmark.
The Role
We're looking for someone who solves business problems with technology. Less stack worship, more outcomes: fast risk identification, fewer false positives, faster time-to-decision, better explainability, and lower cost per case. Finance shows up often, but we think broader — investigations, decisions and client value across industries.
What You'll Do
• Go to the field: talk to users, shadow their workflows, capture the as-is → goals & constraints.
• Define hypotheses & KPIs (precision/recall, FPR, TAT, coverage, cost/decision) and turn them into experiment plans.
• Design decision flows that mix LLMs, retrieval/RAG, classical ML, and lightweight rules; ensure explainability and auditability.
• Build quick prototypes (notebook → lightweight service/API) and measure their impact on real data.
• Create evaluation sets and scoring rubrics (offline + side-by-side + sanity checks + guardrails).
• Present findings & recommendations directly to decision-makers; propose rollout (pilot → production-lite → scale).
• Lead innovation processes across the company; test, promote solutions and mentor others with new AI technologies.
Minimum Requirements
• A track record of delivery: 2–3 examples where your AI/ML solution materially improved process KPIs (any industry).
• Problem-solving & communication: you can break down fuzzy problems and explain risks to non-technical stakeholders.
• LLMs + ML in practice: RAG, prompting, tool-calling; classification/ranking/deduplication; fundamentals of evaluation & experimentation.
• Python + SQL sufficient to build a prototype that works and can be maintained.
• Polish & English fluency for user conversations and concise write-ups.
Nice to Have
• Experience with investigations / trust & safety / fraud / risk / audit or other complex decision processes.
• Graphs/ER: entity resolution, link analysis, pattern-of-life.
• Light engineering craft: FastAPI, Docker; the rest (K8s/CI/CD/Terraform) is not required.
Our Tech (lightweight)
We don't fetishise the stack. Common tools: Python, SQL, notebooks/analysis tools, lightweight APIs (e.g., FastAPI), simple stores (e.g., Postgres), and vector indexes. We choose tools pragmatically — business impact beats heavy infrastructure.
Note: we don't expect mastery of "every" tool. What matters are strong fundamentals, curiosity, and a habit of delivering measurable outcomes.
How We Work
A small, seasoned squad. Weekly goals and regular weekly iterations, a build → measure → learn rhythm. Minimum ceremony, maximum user contact. Decisions are captured briefly — in ADRs, tickets, or short notes.
What We Offer
• Real influence on decisions in matters that actually count.
• Forward-deployed autonomy — you stay close to the problem and solve it end-to-end.
• Remote forever - no offices, no commutes... ever