Applied Data Scientist

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.

We are hiring an Applied Data Scientist to build the models that turn raw transaction data into the insights our banks’ customers see every day.

The stack is deliberately wide: classical ML on tabular data, deep learning, LLM-based systems, and agentic flows. We choose the right approach for each problem, balancing analytical quality, production readiness, and time to market. You drive the analytical work, working closely with Product and Engineering to take ideas from evidence through production.

• Start with the evidence. Frame the problem, review the prior art, and use available data to determine whether the idea holds and is worth pursuing.
• Build the model. Feature engineering and model design on behavioural financial data — gradient-boosted trees, deep learning, an LLM pipeline, or an agentic flow.
• Prove it. Lead the preparation of the model specification, evaluation evidence, and risk documentation required for production in a regulated environment, and support it through review.
• Ship it. Take the solution from POC to live service together with Engineering — integration and controlled roll-out against real customer behaviour.
• Watch it work. Track performance, drift, and business KPIs in production, and keep improving the model on what production shows.

• 2–3 years hands-on as a Data Scientist in a product environment.
• Strong Python (Pandas, NumPy, scikit-learn, PyTorch).
• Classical ML on tabular data — feature engineering, gradient-boosted trees, and model evaluation.
• Hands-on GenAI/LLM application building — RAG, prompt engineering, and evaluation of LLM-based systems.
• Experience building agentic systems — tool use, multi-step workflows, and orchestration.
• Clear technical writing, and the ability to explain a model to a non-technical audience.

• FinTech or banking experience.
• Transformer models — fine-tuning and production inference.
•  Experience taking a model into production, including monitoring and post-launch iteration.
• Cloud platforms (AWS, Azure).