Thinking about swapping your 9‑to‑5 for client work, but worried that a long German–style notice period will kill your chances?  In this live interview, seven‑year data‑freelance veteran Dimitri walks through his experience of taking his freelance career to the next level.

About the Speaker: Dimitri Visnadi is an independent data consultant with a focus on data strategy. He has been consulting companies leading the marketing data space such as Unilever, Ferrero, Heineken, and Red Bull.

He has lived and worked in 6 countries across Europe in both corporate and startup organizations. He was part of data departments at Hewlett-Packard (HP) and a Google partnered consulting firm where he was working on data products and strategy.

Having received a Masters in Business Analytics with Computer Science from University College London and a Bachelor in Business Administration from John Cabot University, Dimitri still has close ties to academia and holds a mentor position in entrepreneurship at both institutions. 🕒 TIMECODES00:00 Dimitri’s journey from corporate to freelance data specialist05:41 Job tenure trends, tech career shifts, and freelance types10:50 Freelancing challenges, success, and finding clients17:33 Freelance market trends and Dimitri’s job board23:51 Starting points, top freelance skills, and market insights32:48 Building a lifestyle business: scaling and work-life balance45:30 Data Freelancer course and marketing for freelancers48:33 Subscription services and managing client relationships56:47 Pricing models and transitioning advice1:01:02 Notice periods, networking, and risks in freelancing transition 🔗 CONNECT WITH DataTalksClub Join the community - https://datatalks.club/slack.html Subscribe to our Google calendar to have all our events in your calendar - https://calendar.google.com/calendar/... Check other upcoming events - https://lu.ma/dtc-events LinkedIn - / datatalks-club
Twitter - / datatalksclub
Website - https://datatalks.club/ 🔗 CONNECT WITH DIMITRI Linkedin - https://www.linkedin.com/in/visnadi/

In this podcast episode, we talked with Tamara Atanasoska about ​building fair AI systems.

About the Speaker:​Tamara works on ML explainability, interpretability and fairness as Open Source Software Engineer at probable. She is a maintainer of fairlearn, contributor to scikit-learn and skops. Tamara has both computer science/ software engineering and a computational linguistics(NLP) background.During the event, the guest discussed their career journey from software engineering to open-source contributions, focusing on explainability in AI through Scikit-learn and Fairlearn. They explored fairness in AI, including challenges in credit loans, hiring, and decision-making, and emphasized the importance of tools, human judgment, and collaboration. The guest also shared their involvement with PyLadies and encouraged contributions to Fairlearn. 00:00 Introduction to the event and the community 01:51 Topic introduction: Linguistic fairness and socio-technical perspectives in AI 02:37 Guest introduction: Tamara’s background and career 03:18 Tamara’s career journey: Software engineering, music tech, and computational linguistics 09:53 Tamara’s background in language and computer science 14:52 Exploring fairness in AI and its impact on society 21:20 Fairness in AI models26:21 Automating fairness analysis in models 32:32 Balancing technical and domain expertise in decision-making 37:13 The role of humans in the loop for fairness 40:02 Joining Probable and working on open-source projects 46:20 Scopes library and its integration with Hugging Face 50:48 PyLadies and community involvement 55:41 The ethos of Scikit-learn and Fairlearn

🔗 CONNECT WITH TAMARA ATANASOSKA Linkedin - https://www.linkedin.com/in/tamaraatanasoska GitHub- https://github.com/TamaraAtanasoska

🔗 CONNECT WITH DataTalksClub Join DataTalks.Club:⁠⁠https://datatalks.club/slack.html⁠⁠ Our events:⁠⁠https://datatalks.club/events.html⁠⁠ Datalike Substack -⁠⁠https://datalike.substack.com/⁠⁠ LinkedIn:⁠⁠  / datatalks-club  

We talked about:

00:00 DataTalks.Club intro

08:06 Background and career journey of Katarzyna

09:06 Transition from linguistics to computational linguistics

11:38 Merging linguistics and computer science

15:25 Understanding phonetics and morpho-syntax

17:28 Exploring morpho-syntax and its relation to grammar

20:33 Connection between phonetics and speech disorders

24:41 Improvement of voice recognition systems

27:31 Overview of speech recognition technology

30:24 Challenges of ASR systems with atypical speech

30:53 Strategies for improving recognition of disordered speech

37:07 Data augmentation for training models

40:17 Transfer learning in speech recognition

42:18 Challenges of collecting data for various speech disorders

44:31 Stammering and its connection to fluency issues

45:16 Polish consonant combinations and pronunciation challenges

46:17 Use of Amazon Transcribe for generating podcast transcripts

47:28 Role of language models in speech recognition

49:19 Contextual understanding in speech recognition

51:27 How voice recognition systems analyze utterances

54:05 Personalization of ASR models for individuals

56:25 Language disorders and their impact on communication

58:00 Applications of speech recognition technology

1:00:34 Challenges of personalized and universal models

1:01:23 Voice recognition in automotive applications

1:03:27 Humorous voice recognition failures in cars

1:04:13 Closing remarks and reflections on the discussion

About the speaker:

Katarzyna is a computational linguist with over 10 years of experience in NLP and speech recognition. She has developed language models for automotive brands like Audi and Porsche and specializes in phonetics, morpho-syntax, and sentiment analysis.

Kasia also teaches at the University of Warsaw and is passionate about human-centered AI and multilingual NLP.

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We talked about:

Eleni’s background Spatial data analytics Responsibilities of a postdoc Publishing papers Best places for data management papers Differences between postdoc and PhD Helping students become successful Research at the DIMA group Identifying important research directions Reviewing papers Underrated topics in data management Research in data cleaning Collaborating with others Choosing the field for Master’s students Choosing the topic for a Master thesis Should I do a PhD? Promoting computer science to female students

Links:

https://www.user.tu-berlin.de/tzirita/

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Our events: https://datatalks.club/events.html