#027 ML Петр Ермаков. ODS - русскоязычное сообщество дата-сайентистов

Item: #027 ML Петр Ермаков. ODS - русскоязычное сообщество дата-сайентистов
Rating: 4
Author: PostSingularity Institute

#027 ML Petr Ermakov. ODS - Russian-speaking data scientist community

by Machine Learning Podcast · View the channel on PSI

Humanities, Social Sciences & Thought AI — Models & Research UYQArtificial intelligence (AI)UYQMMachine learning

🎙 Machine Learning Podcast 👥 5K 📅 July 8, 2021 ⏱ 59 min 👁 1K 📄 expert opinion 🧭 2026-10-09

Available in: English (current) Français

Keywords

ODSdata sciencecommunitymachine learningcareer

Summary

In this episode of Machine Learning Podcast, host Mikhail interviews Pyotr Ermakov, co-founder of the Russian-speaking data science community Open Data Science (ODS) and Senior Data Scientist at Lamoda. Pyotr shares his career journey from childhood programming to working at companies like HeadHunter and Mail.ru, and his current role at Lamoda focusing on recommendation systems. The conversation then turns to ODS: how it originated from informal meetups and a Slack channel, its growth into a large community of tens of thousands, and its culture of mutual help and self-organization. They discuss community rules, including strict moderation, promotion of salary transparency in job postings, and the importance of respectful communication. Key milestones are covered, such as the first DataFest conference, the Dota Science competition, and the community’s takeover of a meetup page. Pyotr also offers practical advice for newcomers, emphasizing the value of asking questions and contributing to the community. The episode provides a behind-the-scenes look at one of the most active data science communities in the CIS region.

168 words

Critical Evaluation

Value of the Information & Strength of the Argument

The episode offers valuable insider knowledge about the ODS community, including its rarely discussed origin story, cultural norms, and operational details. Pyotr’s arguments are grounded in personal experience, which lends authenticity but also introduces subjectivity. He effectively illustrates the community’s spirit through anecdotes, yet the lack of external data or counterpoints limits the argumentative strength. The discussion provides practical takeaways for anyone interested in joining a data science community, making it valuable for career development.

Scientific Rigor, Source Quality, Title Accuracy

Scientific rigour is moderate; the content relies on testimony and informal sources, with no formal references or empirical analysis. The quality of sources is acceptable for an interview format, as it draws on the guest’s direct involvement. The title is fully appropriate and accurately reflects the episode’s focus. The absence of structured citations or verification means the information should be treated as an anecdotal account rather than peer-reviewed knowledge.

159 words

Title / Content Match

The title accurately describes the episode's focus on the ODS community and Pyotr Ermakov's role in it.

Quality & Reliability

7/10

Interview with a co-founder of ODS providing firsthand account of the community's history, culture, and practices. Rich in personal anecdotes but subjective and not peer-reviewed.

Key Moments

Markers derived by PSI from the transcript: the creator did not define chapters.

Cited Sources

Open Data Science community — The main website of the ODS community, referenced as the hub for discussion and resources.
Lessons from participating in the Dota Science hackathon at Data Fest 2 — Habr article described by Pyotr as covering insights from the Dota Science competition.
DataGym school of machine learning — Pyotr's own machine learning school, mentioned as his initiative.
Pyotr's Telegram channel (Power of Data) — Telegram channel shared by Pyotr for further data science content.
This episode on Anchor — Direct link to this podcast episode on Anchor FM.
All episodes on Anchor — Anchor FM page listing all episodes of the Machine Learning Podcast.
Podcast on Apple Podcasts — Apple Podcasts distribution for the show.
Podcast on Google Podcasts — Google Podcasts feed for the podcast.
Podcast on Yandex Music — Yandex Music listing for the podcast.
Telegram channel 'Become a machine learning specialist' — Recommended Telegram channel for those learning machine learning.
Podcast VK page — VK community page for the podcast.

Contribution & Novelties

The video provides a rare insider perspective on the formation and evolution of the Open Data Science community, including candid details about its culture, moderation, and key events. It offers practical advice for community participation, such as how to navigate toxicity and contribute effectively. The originality lies in the firsthand narrative, which is largely undocumented in structured form elsewhere.

Pour aller plus loin :

Community of practice - Wikipedia — Relevant concept for understanding the dynamics of groups like ODS.
Data science - Wikipedia — Core field that the community focuses on.
Open Data Science community — The community itself for direct engagement and further resources.

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Radar Profile

The radar profile shows high quantity of information (8) but moderate quality (7) and low technical depth (4), reflecting an anecdotal, experience-based interview. Reliability is moderate (7), indicating a credible but subjective account. Overall, the episode is informative for community insight but not for technical learning.

Reliability 7/10