HelloWork
Circuit mix
Where HelloWork shows up, by circuit. Each event counts once: 2 if it sponsored, 1 if it only had speakers. Hover a segment for the conference.
- Python & R data science100%2 events
Presence by conference and year
Each cell is one conference in one year. Fill shows the company sponsored, shaded by its best tier that year; a dot means it had speakers there. Hover for details; click to open the event.
- Title
- Platinum
- Gold
- Silver
- Bronze
- Other / unlabeled tier
- Had speakers
| Conference | 2019 | 2020 | 2021 | 2022 | 2023 | 2024 | 2025 | 2026 | 2027 | 2028 |
|---|---|---|---|---|---|---|---|---|---|---|
| PyData |
Activity by year
Three measures on one year axis; each panel has its own scale. 2027, 2028 is still mostly ahead, so its bars are faded and incomplete.
Conferences present at
Distinct conferences each year, split by whether the company sponsored or only had speakers there.
- Sponsored
- Speakers only
Distinct speakers
People from this company on a program that year, each counted once.
Where they've grown or pulled back
HelloWork has never sponsored a tracked conference, so this follows where its speakers appear instead.
DownNothing recorded in 2026; had speakers at 1 conference in 2024.
Counted per conference. 2026 is the latest year whose conferences have mostly happened. A conference counts as no longer seen only when later events published speaker lists.
Not on the company map: lookalikes are computed only for companies present at four or more conferences.
Often sponsors alongside
Companies that sponsored the same events, by number of shared events (out of 0 this company sponsored).
No co-sponsors: this company has no recorded sponsorships, or sponsored alone.
Sponsorships 0
No sponsorships recorded for HelloWork.
Speakers 4
| Speaker | Event | Role | Talk | Links |
|---|---|---|---|---|
| Elizaveta Clouet | PyData Paris 2025 | talk | Balancing Privacy and Utility: Efficient PII Detection and Replacement in Textual Data | — |
| Justine BEL-LETOILE | PyData Paris 2025 | talk | Balancing Privacy and Utility: Efficient PII Detection and Replacement in Textual Data | — |
| Cérès Carton | PyData Paris 2024 | talk | Leveraging LLMs to build supervised datasets suitable for smaller models | — |
| Justine BEL-LETOILE | PyData Paris 2024 | talk | Leveraging LLMs to build supervised datasets suitable for smaller models | — |