AI Data Scientist, LLMs

at  VTT Technical Research Centre of Finland Ltd

ARE YOU INTERESTED IN AI AND LARGE LANGUAGE MODELS (LLMS) AND LOOKING FOR A NEW JOB OPPORTUNITY IN THE FIELD? WOULD YOU LIKE TO DEVELOP INNOVATIVE TECHNOLOGY WITH SUPERB COLLEAGUES WHO ARE TOP EXPERTS IN THEIR FIELD? DO YOU WANT TO BUILD AN EXCELLENT SCIENCE CAREER AND WORK AT THE INTERFACE OF ACADEMY AND INDUSTRY? VTT MIGHT BE JUST THE PLACE FOR YOU.

VTT is one of the leading applied research organisations in Europe. What unites us are curiosity, a passion for learning, and a purpose: to solve the biggest challenges of our time and turn them into sustainable growth and well-being. Our cross-disciplinary community of over 2000 brilliant minds focuses on creating impact through science-based innovation. Get to know us further by exploring VTT’s tech & infra.

WE ARE LOOKING FOR AN AI DATA SCIENTIST TO JOIN OUR QUANTITATIVE SCIENCE AND TECHNOLOGY STUDIES TEAM!

As an AI Data Scientist, your key goals are to carry out world-class research in science and technology studies. Depending on your qualifications, you will have an essential role in the team as an expert in AI, Data science and quantitative analysis, participating in or leading research projects.
Our team focuses on analysing changes in our technological world using large data sets and advancements in natural language processing, generative AI, particularly in the large language models domain, so you will be offered tremendous support with the current solutions and cases we have developed using LLMs.
We’re seeking a hands-on coder to scale our data science and algorithmic solutions at scale into tangible tools and products. In this role, you will also collect broader requirements from other teams to translate our algorithmic and data science solutions to their best use.

WHO WE ARE LOOKING FOR:

You should have either a master’s degree or ongoing/planned doctoral studies. You possess strong data science skills (e.g., machine learning, visualisation, natural language processing, databases, cloud infrastructure, LLMs). A background in Science and Technology Studies is expected, but this can come from different fields such as Industrial Engineering, Economics, Statistics, Social Sciences, Computer Science, or Software Engineering.
In this role, proficiency with cloud platforms such as Microsoft Azure is essential. Hands-on experience with coding (e.g., Python) and software programming concepts is required. Experience in international and EU projects is highly valued.
As a person, we hope that you are a fast learner who is capable of working in a flexible and independent manner, and collaborating with colleagues across different fields of science and technology. We appreciate curiosity, passion as well as the ability to take the long-term perspective and find beyond-the-obvious solutions.

INTERESTED AND WANT TO KNOW MORE?

For further information please contact Research Team Leader Arash Hajikhani, e-mail: arash.hajikhani@vtt.fi or tel. +358 40 9333 549 on Tuesday, August 20, between 14 - 15 or on Tuesday, September 3rd, between 11 - 12 (Finnish time UTC+3).
Arash Hajikhani on X: @Arash_Hajikhani and on
LinkedIn: https://www.linkedin.com/in/arashhajikhani/

As a member of our research team, you will participate in national and international research projects and develop new methods and tools. You will take part in marketing and be involved in the application, planning, and implementation of projects in the field of Science and Technology Studies, particularly focusing on the impact of emerging technology on firms and society more broadly. In this role, you will fluently communicate with and approach a wide range of cooperation partners, including academia, industrial partners, and other interest groups.
The use of AI and generative AI tools and methods to develop LLM-based applications is integral to our daily routine. Designing scalable cloud-based solutions on the current LLM-developed models is core to our operations, and you will be part of using these tools while contributing to the development of LLM-based applications and evaluation methods.