Klaudia Jacek is an experienced accounting professional currently serving as the Accounting Lead and Data Scout at Sociovestix Labs since October 2021, a leading firm in financial data science. Prior to this role, Klaudia worked as a Shop Assistant at Home Bargains from September 2019 to December 2020 and as an Assistant Cook at TIGH-NA-MUIRN LIMITED from July 2016 to September 2019. Klaudia holds a Bachelor's degree in Accounting and Finance from Heriot-Watt University and has completed an HND in Accounting as well as an NC in Travel and Tourism from Dundee and Angus College. Additionally, Klaudia possesses a Level 5 qualification in ESOL and has completed an advanced Matura exam with a focus on Maths, Geography, and English at Liceum im. Stanislawa Staszica w Hrubieszowie.

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Greenock, United Kingdom

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Sociovestix Labs - first in financial data science

Sociovestix Labs Ltd. (SVL) is a socially motivated enterprise founded in July 2012 by four academics with in-depth expertise in artificial intelligence, data science, financial statistics and sustainable finance as a spin-off from the German Research Centre for Artificial Intelligence (DFKI). We have both financial and social aims. Our financial aim is to create value for our clients through the potential of our AI and financial data science technologies. Our social aim is to provide a clear voice to all groups in societies. We achieve this by directly communicating their concerns and capabilities on environmental, social and governance issues to responsible investors. Specifically, we commit to working with any digitally connected, talented individual regardless of residence status. More generally, our work is inspired by the 2015 United Nations’ Global Goals for Sustainable Development. To the best of our knowledge, we were the first team of scientists to set up and join laboratories in the emerging discipline of financial data science. Hence, our credo: we love learning, we empower evidence, we amplify sustainability. We embody Sociovestix Labs: first in financial data science.


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51-200

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