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 seeking an experienced Data Scientist to advance cutting-edge Machine Learning (ML) & Deep Learning (DL) research, champion Responsible AI best practices, and drive product innovation. This role offers the opportunity to develop and optimize ML models while also pioneering innovative AI applications under strict governance and ethical standards. You’ll work on both structured, production‑ready AI systems and experimental, next‑generation AI solutions, adapting based on business needs.

If you love rigorous ML development, creative AI prototyping, AND ensuring AI is built responsibly, this is the perfect role for you!

Responsibilities:

ML/DL Research & Applied Data Science

• Develop, optimize, and deploy ML/DL models aligned with business goals.
• Conduct data exploration, preprocessing, and model enhancement to improve performance.
• Implement robust model monitoring pipelines to ensure reliability and mitigate drift.
• Collaborate with engineering & product teams to integrate AI solutions into production systems.

New Product & Backlog Research

• Scout emerging technology trends and conduct market & internal backlog research to surface unmet customer needs.
• Evaluate and prioritize research findings, translating them into actionable, data‑driven proposals that influence the product roadmap.

Requirements:

• 6–10 years of end-to-end Data-Science ownership, from exploration through production monitoring.
• Proven experience from FinTech or Banking domains.
• Expert in classical ML techniques (gradient-boosted trees, feature engineering, time-series methods)
• Proven track record with transformer models (BERT, RoBERTa, DeBERTa, etc.) for classification, NER, summarization, or QA, including fine-tuning and production inference.
• Hands-on GenAI tooling — LLMs, RAG pipelines, vector DBs, context engineering.