Data Scientist Interne at Swissquote

Gland, Vaud, Switzerland -

Full Time

Start Date

Immediate

Expiry Date

12 Mar, 26

Salary

0.0

Posted On

12 Dec, 25

Experience

0 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Data Science, NLP, LLMs, Fine-Tuning, Model Compression, Python, PyTorch, TensorFlow, Banking, Communication, Teamwork, Experiment Design, Model Performance, AI Assistants, Curating Datasets, Parameter-Efficient Methods

Industry

Banking

Description

Company Description Building the bank of tomorrow takes more than skills. It means combining our differences to imagine, discuss, code, develop, test, learn… and celebrate every step together. Share our vibes? Join Swissquote to unleash your potential. We are the Swiss Leader in Online Banking and we provide trading, investing and banking services to +500’000 clients through our performant and secured digital platforms. Our +1000 employees work in a flexible way, without dress code and in multicultural teams. By having a huge impact on the industry, they are growing their skills portfolio and boosting their career in a fast-pace environment We are all in at Swissquote. As an equal opportunity employer, we welcome candidates from all backgrounds, experiences and perspectives to join our team and contribute to our shared success. Follow Humans of Swissquote to discover our people & culture! Join our 240 software engineers to challenge the code and bring your expertise on cutting-edge Fintech projects such as eTrading, eForex, cryptocurrencies, the Yuh app and more. Our Tech stack is 100% Cobol free! See: Java, Spring, Hibernate, JavaScript, TypeScript, React, Docker, Kubernetes... and we always try to find the best tools for the right needs. Have a look at our Tech Blog! Are you all in? Don’t be shy, apply! Job Description Shaping the future of banking with AI: Distilling and optimizing Large Language Models for AI assistants in finance and banking The integration of financial services with technology has created an overwhelming amount of structured and unstructured data. To turn this into reliable, real-time intelligence, we need efficient and well-optimized language models, not just raw scale. Swissquote's Data Science team offers an internship focused on distillation, fine-tuning, and parameter-efficient adaptation (e.g. LoRA) of Large Language Models (LLMs) for production-grade AI assistants. The main objective is to explore, design, and refine model compression and adaptation techniques to build fast, cost-efficient, and robust AI assistants tailored to the banking and finance domains. The DS team is integrated in Swissquote's core research group, the Quantitative Research & Solutions (QRS) department, specialized in theoretical and applied research in Quantitative Finance and Data Science. QRS's mission is to provide best-in-class quantitative and AI solutions by performing research to support every division of the bank in need of an analytical and data-driven approach. By Joining The Team As Data Scientist Intern You Will : Prepare and curate financial text datasets for training, fine-tuning, and evaluation Apply and benchmark LLM distillation techniques to build smaller, faster chatbot models Use LoRA and other parameter-efficient fine-tuning methods to adapt LLMs to banking-specific tasks Design experiments, evaluate model performance, and help integrate optimized models into AI assistant pipelines Qualifications Solid background in data science and NLP Prior exposure to, or a strong interest in, LLMs, fine-tuning, and/or model compression Coding proficiency in Python (PyTorch / TensorFlow or similar is a plus) Interest in the banking and financial sectors Creative and curious, comfortable questioning existing approaches Organized, self-motivated with excellent communication skills Ability to work in a team and communicate with different stakeholders Fluent in English

Responsibilities

The intern will prepare and curate financial text datasets, apply LLM distillation techniques, and design experiments to evaluate model performance. They will also help integrate optimized models into AI assistant pipelines.