Data Scientist
Summary
Build and deploy production AI systems and LLM workflows for banking, including credit risk and compliance, using Python, vLLM, and fine-tuning techniques.
What you'll do:
- Own production AI systems and LLM workflows end to end: design, build, deploy, monitor
- Build the evaluation layer that decides what ships: offline evals, A/B tests, live monitoring
- Optimize inference with vLLM, prompt optimization frameworks, and fine-tuning (LoRA/QLoRA)
- Work alongside experienced teams in Product, Risk, and Compliance to apply data science techniques to real-world banking scenarios, including credit risk and regulatory compliance.
We’re looking for curious and eager-to-learn candidates who are:
- Having 3+ years experiences on building ML or AI systems, including at least one LLM or RAG system you took to production
- Skilled in translating technical trade-offs into actionable insights for non-technical stakeholders (for example: Product, Risk, CX, and Compliance)
- Strong Python fundamentals
- Proven experience in conducting evaluations that directly influenced key product decisions
Bonus points (not required):
- Banking or fintech background
- High-throughput LLM serving with vLLM
- GEPA or similar prompt optimization in practice
- Fine-tuning for financial use cases