Join us to solve hard problems using the full spectrum of modern AI. We are looking for a Data Scientist who can seamlessly switch between training deep neural networks from scratch and orchestrating complex, agentic LLM workflows. This role takes you beyond classical fintech, challenging you to apply advanced AI to product data and comparable shopping. It is a hands-on, technical position where theoretical depth meets engineering reality, and where you will build autonomous systems and custom models that drive real business value.

What You Will Do

Build & Deploy Models: Own full design, training, and deployment of Neural Network models using frameworks like TensorFlow or PyTorch to solve diverse business challenges.
Develop Agentic Workflows: Architect and implement autonomous AI agents capable of reasoning, planning, and executing multi-step tasks to automate complex workflows.
Leverage LLMs: Fine-tune and orchestrate Large Language Models for varied use cases, integrating them into robust applications.
Bridge Theory & Practice: Apply a strong theoretical understanding of Machine Learning to practical problems, ensuring solutions are not just academically sound but robust, scalable, and valuable to the business.
End-to-End Ownership: Take ownership of the full ML lifecycle, from data pipeline construction and feature engineering to model monitoring and continuous improvement in production.
Collaborate & Innovate: Work closely with engineers and product teams to identify opportunities where advanced AI can drive significant impact, while staying ahead of the curve on SOTA research.

Who You Are

Strong Technical Foundation: You have a solid grasp of computer science fundamentals and software engineering principles. You write clean, production-ready code (Python is a must).
Deep Learning Expertise: You have hands-on experience building and training Deep Neural Networks and are proficient with deep learning frameworks such as TensorFlow, Keras, or PyTorch.
LLM & Agentic Experience: You are familiar with the modern LLM stack (e.g., Hugging Face, OpenAI API) and have experimented with or built agentic systems (using tools like LangChain, AutoGen, or custom implementations).
Pragmatic & Theoretical: You balance deep theoretical knowledge of how models work (mathematics, optimization, probability) with a pragmatic "get it done" attitude. You know when to use a simple regression and when to deploy a Transformer.
Generalist Mindset: You are adaptable and eager to apply data science to various domains, rather than being niche-focused.
Education: A degree in Computer Science, Mathematics, Physics, or a related quantitative field (or equivalent practical experience).

Awesome to have

Experience deploying ML models on cloud infrastructure (AWS, GCP) using Docker and Kubernetes.
Knowledge of MLOps best practices and tools.
Experience with vector databases (Pinecone, Milvus, Weaviate) and RAG (Retrieval-Augmented Generation) architectures.
Contributions to open-source AI/ML projects or a portfolio of personal projects demonstrating your ability to build complex systems.
Familiarity with reinforcement learning or graph neural networks.

Please include CV in english!