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

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🔗 CONNECT WITH DIMITRI

Linkedin - https://www.linkedin.com/in/visnadi/

In this podcast episode, we talked with Will Russell about From Hackathons to Developer Advocacy.

About the Speaker:

Will Russell is a Developer Advocate at Kestra, known for his videos on workflow orchestration. Previously, Will built open source education programs to help up and coming developers make their first contributions in open source. With a passion for developer education, Will creates technical video content and documentation that makes technologies more approachable for developers.

In this episode, we sit down with Will—developer advocate, content creator, and passionate community builder. We’ll hear about his unique path through tech, the lessons he’s learned, and his approach to making complex topics accessible and engaging. Whether you’re curious about open source, hackathons, or what it’s like to bridge the gap between developers and the broader tech community, this conversation is full of insights and inspiration.

🕒 TIMECODES

0:00 Introduction, career journeys, and video setup and workflow

10:41 From hackathons to open source: Early experiences and learning

16:04 Becoming a hackathon organizer and the value of soft skills

23:18 How to organize a hackathon, memorable projects, and creativity

33:39 Major League Hacking: Building community and scaling student programs

41:16 Mentorship, development environments, and onboarding in open source

49:14 Developer advocacy, content strategy, and video tips

57:16 Will’s current projects and future plans for content creation

🔗 CONNECT WITH DataTalksClub

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🔗 CONNECT WITH WILL

LinkedIn - https://www.linkedin.com/in/wrussell1999/

Twitter - https://x.com/wrussell1999

GitHub - https://github.com/wrussell1999

Website - https://wrussell.co.uk/

In this podcast episode, we talked with Lavanya Gupta about Building a Strong Career in Data.

About the Speaker:

Lavanya is a Carnegie Mellon University (CMU) alumni of the Language Technologies Institute (LTI). She works as a Sr. AI/ML Applied Associate at JPMorgan Chase in their specialized Machine Learning Center of Excellence (MLCOE) vertical. Her latest research on long-context evaluation of LLMs was published in EMNLP 2024.

In addition to having a strong industrial research background of 5+ years, she is also an enthusiastic technical speaker. She has delivered talks at events such as Women in Data Science (WiDS) 2021, PyData, Illuminate AI 2021, TensorFlow User Group (TFUG), and MindHack! Summit. She also serves as a reviewer at top-tier NLP conferences (NeurIPS 2024, ICLR 2025, NAACL 2025). Additionally, through her collaborations with various prestigious organizations, like Anita BOrg and Women in Coding and Data Science (WiCDS), she is committed to mentoring aspiring machine learning enthusiasts.

In this episode, we talk about Lavanya Gupta’s journey from software engineer to AI researcher. She shares how hackathons sparked her passion for machine learning, her transition into NLP, and her current work benchmarking large language models in finance. Tune in for practical insights on building a strong data career and navigating the evolving AI landscape.

🕒 TIMECODES

00:00 Lavanya’s journey from software engineer to AI researcher

10:15 Benchmarking long context language models

12:36 Limitations of large context models in real domains

14:54 Handling large documents and publishing research in industry

19:45 Building a data science career: publications, motivation, and mentorship

25:01 Self-learning, hackathons, and networking

33:24 Community work and Kaggle projects

37:32 Mentorship and open-ended guidance

51:28 Building a strong data science portfolio

🔗 CONNECT WITH LAVANYALinkedIn -   / lgupta18  🔗 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/

In this podcast episode, we talked with Eddy Zulkifly about From Supply Chain Management to Digital Warehousing and FinOps

About the Speaker:

In this podcast episode, we talked with Bartosz Mikulski about Data Intensive AI.

About the Speaker:

Bartosz is an AI and data engineer. He specializes in moving AI projects from the good-enough-for-a-demo phase to production by building a testing infrastructure and fixing the issues detected by tests. On top of that, he teaches programmers and non-programmers how to use AI. He contributed one chapter to the book 97 Things Every Data Engineer Should Know, and he was a speaker at several conferences, including Data Natives, Berlin Buzzwords, and Global AI Developer Days. 

In this episode, we discuss Bartosz’s career journey, the importance of testing in data pipelines, and how AI tools like ChatGPT and Cursor are transforming development workflows. From prompt engineering to building Chrome extensions with AI, we dive into practical use cases, tools, and insights for anyone working in data-intensive AI projects. Whether you’re a data engineer, AI enthusiast, or just curious about the future of AI in tech, this episode offers valuable takeaways and real-world experiences.

0:00 Introduction to Bartosz and his background

4:00 Bartosz’s career journey from Java development to AI engineering

9:05 The importance of testing in data engineering

11:19 How to create tests for data pipelines

13:14 Tools and approaches for testing data pipelines

17:10 Choosing Spark for data engineering projects

19:05 The connection between data engineering and AI tools

21:39 Use cases of AI in data engineering and MLOps

25:13 Prompt engineering techniques and best practices

31:45 Prompt compression and caching in AI models

33:35 Thoughts on DeepSeek and open-source AI models

35:54 Using AI for lead classification and LinkedIn automation

41:04 Building Chrome extensions with AI integration

43:51 Comparing Cursor and GitHub Copilot for coding

47:11 Using ChatGPT and Perplexity for AI-assisted tasks

52:09 Hosting static websites and using AI for development

54:27 How blogging helps attract clients and share knowledge

58:15 Using AI to assist with writing and content creation

🔗 CONNECT WITH Bartosz

LinkedIn: https://www.linkedin.com/in/mikulskibartosz/

Github: https://github.com/mikulskibartosz

Website: https://mikulskibartosz.name/blog/

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In this podcast episode, we talked with Nemanja Radojkovic about MLOps in Corporations and Startups.

About the Speaker:

Nemanja Radojkovic is Senior Machine Learning Engineer at Euroclear.

In this event,we’re diving into the world of MLOps, comparing life in startups versus big corporations. Joining us again is Nemanja, a seasoned machine learning engineer with experience spanning Fortune 500 companies and agile startups. We explore the challenges of scaling MLOps on a shoestring budget, the trade-offs between corporate stability and startup agility, and practical advice for engineers deciding between these two career paths. Whether you’re navigating legacy frameworks or experimenting with cutting-edge tools.

1:00 MLOps in corporations versus startups

6:03 The agility and pace of startups

7:54 MLOps on a shoestring budget

12:54 Cloud solutions for startups

15:06 Challenges of cloud complexity versus on-premise

19:19 Selecting tools and avoiding vendor lock-in

22:22 Choosing between a startup and a corporation

27:30 Flexibility and risks in startups

29:37 Bureaucracy and processes in corporations

33:17 The role of frameworks in corporations

34:32 Advantages of large teams in corporations

40:01 Challenges of technical debt in startups

43:12 Career advice for junior data scientists

44:10 Tools and frameworks for MLOps projects

49:00 Balancing new and old technologies in skill development

55:43 Data engineering challenges and reliability in LLMs

57:09 On-premise vs. cloud solutions in data-sensitive industries

59:29 Alternatives like Dask for distributed systems

🔗 CONNECT WITH NEMANJA

LinkedIn -   / radojkovic  

Github - https://github.com/baskervilski

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Join the community - https://datatalks.club/slack.html

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In this podcast episode, we talked with Adrian Brudaru about ​the past, present and future of data engineering.

About the speaker:

In this podcast episode, we talked with Alexander Guschin about launching a career off Kaggle.

About the Speaker:

In this podcast episode, we talked with Andrey Cheptsov about ​The future of AI infrastructure.

About the Speaker:

Andrey Cheptsov is the founder and CEO of dstack, an open-source alternative to Kubernetes and Slurm, built to simplify the orchestration of AI infrastructure. Before dstack, Andrey worked at JetBrains for over a decade helping different teams make the best developer tools.

During the event, the guest, Andrey Cheptsov, founder and CEO of dstack, discussed the complexities of AI infrastructure. We explore topics like the challenges of using Kubernetes for AI workloads, the need to rethink container orchestration, and the future of hybrid and cloud-only infrastructures. Andrey also shares insights into the role of on-premise and bare-metal solutions, edge computing, and federated learning.

00:00 Andrey's Career Journey: From JetBrains to DStack

5:00 The Motivation Behind DStack

7:00 Challenges in Machine Learning Infrastructure

10:00 Transitioning from Cloud to On-Prem Solutions

14:30 Reflections on OpenAI's Evolution

17:30 Open Source vs Proprietary Models: A Balanced Perspective

21:01 Monolithic vs. Decentralized AI businesses

22:05 The role of privacy and control in AI for industries like banking and healthcare

30:00 Challenges in training large AI models: GPUs and distributed systems

37:03 DeepSpeed's efficient training approach vs. brute force methods

39:00 Challenges for small and medium businesses: hosting and fine-tuning models

47:01 Managing Kubernetes challenges for AI teams

52:00 Hybrid vs. cloud-only infrastructure

56:03 On-premise vs. bare-metal solutions

58:05 Exploring edge computing and its challenges

🔗 CONNECT WITH ANDREY CHEPTSOV

Twitter -  / andrey_cheptsov  

Linkedin -  / andrey-cheptsov  

GitHub - https://github.com/dstackai/dstack/

Website - https://dstack.ai/

🔗 CONNECT WITH DataTalksClub

Join DataTalks.Club:⁠⁠⁠https://datatalks.club/slack.html⁠⁠⁠

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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

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Datalike Substack -⁠⁠https://datalike.substack.com/⁠⁠

LinkedIn:⁠⁠  / datatalks-club