GenAI Data Scientist

About the company

Personetics is shaping the Cognitive Banking era, harnessing AI to help banks anticipate customer needs, provide actionable insights, and deliver intelligent financial guidance. Our platform continuously analyzes and leverages real-time transactional data, enabling banks to proactively support customers in managing their finances and reaching their goals.

As industry leaders — yes, we really are leaders — we partner with the world’s top financial institutions, empowering over 150 million customers monthly across 35 global markets from offices in New York, London, Singapore, São Paulo, and Tel Aviv.

About the position

We are looking for a forward-thinking Data Scientist with a passion for Generative AI (GenAI) and emerging AI technologies. This role is ideal for those who thrive in rapid prototyping, proof-of-concept (PoC) development, and iterative experimentation. You will work across multiple projects, transforming innovative ideas into practical AI solutions while engaging with clients for feedback and improvement.

Responsibilities

• Generative AI Innovation & Research: Design PoCs with LLMs, RAG, RL, and agent-based AI. Apply emerging AI methods to real-world business needs.
• Prototyping & Implementation: Translate research into scalable prototypes and production solutions. Ensure models are efficient, adaptable, and enterprise-ready. Lead integration, A/B testing, and phased rollouts.
• AI Engineering & Deployment: Architect scalable GenAI solutions for banking. Own the full lifecycle: deployment, monitoring, incident response. Build dashboards, detect model drift, and integrate feedback loops.

Requirements

• 3–4 years in Data Science, ML, or AI research.
• Hands-on experience with LLMs, content engineering, and GenAI applications.
• Demonstrated success operating AI/ML systems in production, including monitoring, logging, and continuous improvement.
• RAG expertise (retrieval-augmented generation) for AI enhancement.
• Experience architecting and orchestrating agent-based LLM workflows (e.g., LangGraph) to automate multi-step tasks.
• Familiarity with the Model Context Protocol (MCP) for standardized context and tool integration between GenAI models and external services.