DataTalks.Club

In this talk, Tatiana, Staff Software Engineer at LinkedIn, shares her journey from academic physics to becoming a Kaggle Master and winning the Sound Demixing Challenge. We explore how to use machine learning competitions as a strategic tool to build a high-impact career and bridge the gap between theory and production.You’ll learn about:
Turning competition code into professional GitHub repos.
Converting results into papers for NIPS and CVPR.
How LLMs are changing the benchmark for AI competitions.
Why hands-on implementation beats passive learning.
Using Topcoder and AI Crowd for research-driven goals.
Practical steps for your very first model submission.Links:
Rise: 3 Practical Steps for Advancing Your Career, Standing Out as a Leader, and Liking Your Life. By Patty Azzarello https://www.porchlightbooks.com/pages/author/Patty_Azzarello-16156396 - awesome book about why doing good is not enough, and what else you need to do to promote your career (same applies to competitions)
AICrowd - https://www.aicrowd.com/challenges
Grand challenges - https://grand-challenge.org/challenges/
Kaggle competitions - https://www.kaggle.com/competitions
TopCoder challenge SpaceNet 9 - https://www.topcoder.com/challenges/9620f66a-767e-40ac-81d5-5cc61274b186(no current active competitions, but they appear)
Medium blog post with instruction - https://medium.com/data-science/writing-papers-tech-reports-after-kaggle-competitions-ee504fc0c4c1
Kaggle Solution Write-Up Documentation - https://www.kaggle.com/solution-write-up-documentation
Evaluating Machine Learning Agents on Machine Learning Engineering - https://arxiv.org/abs/2410.07095
Machine Learning Engineering Agent via Search and Targeted Refinement - https://arxiv.org/html/2506.15692v2
AI Research Agents for Machine Learning: Search, Exploration, and Generalization in MLE-bench - chrome-extension://efaidnbmnnnibpcajpcglclefindmkaj/https://arxiv.org/pdf/2507.02554TIMECODES:00:00 Tatiana’s journey from academia to staff software engineer06:01 Machine learning applications in physics and signal processing09:13 Skill development and domain diversification on Kaggle13:35 Agentic AI benchmarks and automated competition entries17:43 Deep technical mastery versus leaderboard gamification23:04 Hands-on implementation and the illusion of learning26:01 Specialized platforms and fair competition environments31:35 Academic publications and research from silver medals35:24 GitHub repositories and engineering portfolio building39:02 Technical marketing via blog posts and LinkedIn43:25 Innovative approaches for academic conference submissions47:21 Research challenges at NIPS and CVPR workshops52:51 Medical imaging platforms and specialized recommendations57:46 First submission strategies for beginners01:00:56 Asynchronous collaboration and competition team dynamicsPerfect for data scientists and engineers looking to transition from academia or build a formal portfolio using Kaggle as a career-advancement tool.Connect with Tatiana:
Linkedin - https://www.linkedin.com/in/tatigabru/

At PyConDE 2026, community leaders, educators, and Python tooling builders explored how Python is evolving in the age of AI — and why human connection, mentorship, and strong fundamentals matter more than ever.
Jessica Greene (Ecosia / PyLadies Berlin) spoke about her work as a machine learning engineer and community organizer. She highlighted PyLadies Berlin’s role in creating inclusive spaces for learning, networking, and career growth, and emphasized that AI should be seen as an amplification tool—not a replacement for solid engineering or people skills.
Cheuk Ting Ho (JetBrains) discussed her role on the PyCharm team, where conferences are key for gathering feedback and staying connected to the community. She shared insights from her talk on free-threaded Python and her approach to technical storytelling across talks, blogs, videos, and informal interviews.
Sebastian Raschka reflected on his work as an AI educator focused on “from scratch” explanations of machine learning and LLMs. Driven by curiosity, he prefers creating new talks over repeating old ones and aims to help people understand what happens under the hood—especially with reasoning models.
Kyle Into (Meta) introduced Pyrefly, a Rust-based Python type checker designed for large codebases. He explained how type checking improves both human and AI-assisted development by making interfaces explicit, reducing risk, and strengthening project structure.
Valerio Maggio shared his journey from data science into developer advocacy and community organizing. He emphasized that conferences rely on volunteers, that lightning talks boost accessibility and energy, and that sustainable processes are essential to avoid burnout.
Tereza Iofciu discussed her “Data Diplomat” coaching framework, helping data professionals navigate leadership and uncertainty. She noted that AI and lean teams are raising expectations, making it crucial to think strategically, build fundamentals, and invest in real networks.
Irina Saribekova described her transition from organizing Python events in Saint Petersburg to supporting PyData Berlin and PyConDE. She highlighted that conferences are built on trust, relationships, and clear systems—and that developer relations extends this work through talks, writing, and community engagement.

Jessica Greene
Machine Learning Engineer at Ecosia, PyLadies Berlin co-organizer, and chair of the PyLadies Germany fund.
Connect: ⁠https://www.linkedin.com/in/jessica0greene/⁠

Cheuk Ting Ho
Developer Advocate at JetBrains working with the PyCharm team and active in the global Python community.
Connect: ⁠https://www.linkedin.com/in/cheukting-ho/⁠

Sebastian Raschka
AI educator, author, and machine learning researcher focused on LLMs, reasoning models, and educational “from scratch” implementations.
Connect: ⁠https://www.linkedin.com/in/sebastianraschka/⁠

Kyle Into
Engineer at Meta working on Pyrefly, a fast Python type checker built for large-scale codebases and AI-assisted development.
Connect: ⁠https://www.linkedin.com/in/kyleinto/

⁠Valerio Maggio
Data scientist, developer advocate, community organizer, and long-time contributor to PyCon Italia andPyConDE.
Connect: ⁠https://www.linkedin.com/in/valeriomaggio/⁠

Tereza Iofciu
Data coach, trainer, community contributor, and creator of the Data Diplomat framework for data professionals and leaders.
Connect: ⁠https://www.linkedin.com/in/tereza-iofciu/⁠

Irina Saribekova
Developer relations specialist and Python community organizer involved in PyData Berlin, PyConDE, and conference community building.
Connect: ⁠https://www.linkedin.com/in/irinasaribekova/⁠

In this talk, Leonid Kholkine, Head of Research & Development at Their Data and Co-founder of Data Makers Fest, shares his unique journey from leading international student organizations to building one of Europe’s premier data conferences. We explore the behind-the-scenes reality of community building, the evolution of the Portuguese data scene, and the technical challenges of managing AI observability at an enterprise scale.You’ll learn about:- Understanding the hybrid role between product engineering and high-touch consultancy.ow organizing meetups and leagues creates a professional reputation and high-trust networks.- The hidden complexities of moving from local meetups to large-scale international conferences (venues, AV, and timing).- How Leonid used custom code and embeddings to automate speaker scheduling and timetable optimization.- Why community is the essential antidote for data practitioners working as the "only one" in their company.- A look into R&D at Their Data and the future of monitoring and self-improving generative AI workflows.Links: - www.datamakersfest.com- Data Lead Club - http://dataleadclub.ripply.net/- DareData - https://www.daredata.ai/- GenOS by DareData - https://www.daredata.ai/gen-osTIMECODES:00:00 Community Building in Data and AI03:02 Computer Engineering and International Leadership Roots06:13 Machine Learning Research in Sports Physiology10:18 Data Lead Club and Executive Networking Retreats14:03 AI Observability and R&D at Their Data18:50 Professional Growth through Community Organizing22:11 The Origins of Data Science Portugal27:57 Logistical Challenges of In-Person Conferences31:24 Strategic Event Scheduling and Venue Selection36:52 Automated Timetable Optimization with Custom Code41:22 Curating Quality Speaker Proposals in the AI Era45:08 Sponsorship Value and Student Ticket Accessibility50:23 Partnership Outreach and Network Development54:44 The Forward Deployed Engineer Role and Methodology58:35 Professional Development for Junior Data ScientistsThis video is a must-watch for data practitioners, aspiring community leaders, and event organizers. It provides deep value for anyone looking to understand the intersection of technical R&D and the "human stack" of networking and professional development.Connect with Leonid- Linkedin - https://www.linkedin.com/in/kholkine/Connect with DataTalks.Club:- 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/r?cid=ZjhxaWRqbnEwamhzY3A4ODA5azFlZ2hzNjBAZ3JvdXAuY2FsZW5kYXIuZ29vZ2xlLmNvbQ- Check other upcoming events - https://lu.ma/dtc-events- GitHub: https://github.com/DataTalksClub- LinkedIn - https://www.linkedin.com/company/datatalks-club/ - Twitter - https://twitter.com/DataTalksClub - Website - https://datatalks.club/

In this talk, Nasser Qadri, AI Engineering Manager at Google, shares his unique career journey—from a PhD in Politics and International Relations to leading high-stakes AI initiatives. We explore the evolution of the AI Engineer role, the critical intersection of social science and machine learning, and how to build robust agentic workflows with engineering rigor.You’ll learn about:- Moving beyond simple API calls to implementing full-stack engineering principles and "Agent Ops."- How a background in qualitative research and statistics provides a unique "moral compass" for building ethical AI.- A strategic roadmap for transitioning from non-traditional backgrounds into elite AI engineering roles.- Using design thinking and personal "pain points" to drive meaningful technical innovation.- Why traditional ML and model distillation will remain vital as we move from generalist LLMs to specialized, high-speed agents.- How to navigate the complex landscape of AI frameworks and build depth in your technical stack.TIMECODES:00:00 Transitioning from Social Science to Software Engineering07:45 Applying Statistical Rigor to Generative AI Evaluation12:10 Balancing Research Mindsets with Engineering Speed16:30 Managing Non-Deterministic Systems and Model Creativity20:15 Comparing AI Roles in Big Tech vs Startups24:40 Learning by Building: Solving Personal Pain Points31:50 Mental Frameworks for Problem Finders and Solvers36:15 Human-Centered Design in the Age of LLMs42:05 Beyond API Calls: Software Engineering Rigor for Agents45:50 Orchestration and the Rise of Agent Ops51:30 Depth vs Breadth in AI Framework Selection56:10 The Future of Latency and Traditional ML Integration1:01:20 When to Prioritize Model Distillation and Fine-Tuning1:02:10 Closing Thoughts and Future OutlookThis conversation is designed for software engineers, data scientists, and career-switchers looking to transition into the Generative AI space. It is particularly valuable for technical leaders in large organizations and startups who need to balance rapid AI prototyping with long-term system reliability.Connect with Nasser- Linkedin - https://www.linkedin.com/in/nasserq/Connect with DataTalks.Club:- 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/r?cid=ZjhxaWRqbnEwamhzY3A4ODA5azFlZ2hzNjBAZ3JvdXAuY2FsZW5kYXIuZ29vZ2xlLmNvbQ- Check other upcoming events - https://lu.ma/dtc-events- GitHub: https://github.com/DataTalksClub- LinkedIn - https://www.linkedin.com/company/datatalks-club/ - Twitter - https://twitter.com/DataTalksClub - Website - https://datatalks.club/

In this talk, Slawomir Tulski, Data Leadership Consultant and former Meta Data Engineering Manager, shares his ten-year journey through the evolution of data systems—from researching glaciers in Poland to scaling the ads ranking infrastructure at one of the world's largest tech giants. We explore the shifting definition of the Data Engineer, the "Actionable Data" philosophy, and how to navigate the 2026 hiring market amidst the rise of AI.You’ll learn about:- How to distinguish between Platform DE, Product DE, and Analytics Engineering.- Why most teams over-engineer their stacks and how to build "Value-First" instead of "Tool-First."- Why being "cloud-cost-conscious" is the most underrated competitive advantage in modern data teams.- How to identify "Legacy Traps" and choose a company culture that fosters growth.- Why strategic builders will thrive while "DBT Monkeys" and manual triaging roles are at risk of automation.- How to frame side projects and end-to-end "Toy Platforms" to stand out to recruiters without a Big Tech pedigree.TIMECODES:00:00 From Measuring Glaciers to London’s Tech Scene06:47 Hadoop vs. AI: Lessons from the Original Big Data Hype11:54 The Data Identity Crisis: Platform vs. Product Engineering17:29 Tech-Native vs. Tech-by-Necessity Company Cultures25:33 The Competitive Advantage of Cost-Aware Engineering30:56 Avoiding Over-Engineered Platforms and Modern Data Stacks38:01 The Real-Time Myth: When to Use Kafka and Spark42:08 Breaking into Data Engineering: 2026 Market Reality51:04 AI Automation: Why Strategic Builders Outlast "DBT Monkeys"57:35 Portfolio Strategy: Framing Side Projects for Maximum Impact1:04:42 The Ultimate Portfolio Project: Building End-to-End Platforms1:07:49 Networking Advice and Local Gdansk CultureThis talk is designed for ambitious data professionals including engineers, analysts, and career-switchers who want a pragmatic, "fluff-free" roadmap for surviving and thriving in the 2026 data landscape. It is particularly valuable for hiring managers and senior leaders looking to audit their recruitment processes, as well as those in traditional corporate environments seeking to implement the agile, high-impact engineering cultures found in Big Tech giants like Meta.Connect with Slawomir:- Linkedin - https://www.linkedin.com/in/slawomir-tulski-091611116/- Form for DE role Ebook - https://docs.google.com/forms/d/e/1FAIpQLSdSCLaBdTtuRlgV_nukKckumR60VOovECtlRIRI5DMUIk36EQ/viewform?usp=dialogConnect with DataTalks.Club:- 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/r?cid=ZjhxaWRqbnEwamhzY3A4ODA5azFlZ2hzNjBAZ3JvdXAuY2FsZW5kYXIuZ29vZ2xlLmNvbQ- Check other upcoming events - https://lu.ma/dtc-events- GitHub: https://github.com/DataTalksClub- LinkedIn - https://www.linkedin.com/company/datatalks-club/ - Twitter - https://twitter.com/DataTalksClub - Website - https://datatalks.club/