QuantCo
Circuit mix
Where QuantCo 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 science94%10 events
- Open-source data infrastructure6%1 event
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 | ||||||||||
| Berlin Buzzwords |
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.
Sponsorships by tier
Sponsorship rows per year (one per event and listing), stacked by normalized tier.
- Platinum
- Gold
- Silver
Where they've grown or pulled back
FlatSponsored 1 conference in 2026, flat from 1 in 2023.
Counted per conference. 2026 is the latest year whose conferences have mostly happened. Tiers use the best tier each year; swatches show the path, oldest first. A conference counts as pulled out only when a later event published a complete sponsor list, since most events don’t say whether their list is complete.
Moved up 1
Best tier rose between the first and latest year with a ranked tier.
- PyDataGold 2022 → Platinum 2026
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 7 this company sponsored).
| Company | Shared events | Share |
|---|---|---|
| EuroPython Society | 5 | 71% |
| PyLadies | 5 | 71% |
| Rosen | 5 | 71% |
| Blue Yonder | 4 | 57% |
| energy & meteo systems GmbH | 4 | 57% |
| Hopsworks | 4 | 57% |
| inovex GmbH | 4 | 57% |
| JetBrains | 4 | 57% |
| PyData Berlin | 4 | 57% |
| PyData Südwest | 4 | 57% |
| Python Software Foundation | 4 | 57% |
| ZEISS | 4 | 57% |
| Anaconda | 3 | 43% |
| Coiled | 3 | 43% |
| DB Systel GmbH | 3 | 43% |
Sponsorships 7
| Event | Date | Place | Tier as listed | Normalized tier |
|---|---|---|---|---|
| PyCon DE & PyData 2026 | Apr 14–16, 2026 | Darmstadt, Germany | Platinum | Platinum |
| PyData Paris 2025 | Sep 30, 2025 – Oct 2, 2025 | Paris, France | Silver | Silver |
| PyCon DE & PyData 2025 | Apr 23–25, 2025 | Darmstadt, Germany | Platinum | Platinum |
| PyCon DE & PyData Berlin 2024 | Apr 22–24, 2024 | Berlin, Germany | Platinum | Platinum |
| PyCon DE & PyData Berlin 2023 | Apr 17–19, 2023 | Berlin, Germany | Platinum | Platinum |
| PyData London 2022 | Jun 17–19, 2022 | London, United Kingdom | Gold | Gold |
| PyCon DE & PyData Berlin 2022 | Apr 11–13, 2022 | Berlin, Germany | Gold | Gold |
Speakers 13
| Speaker | Event | Role | Talk | Links |
|---|---|---|---|---|
| Andreas Albert | PyCon DE & PyData 2026 | tutorial | Building reliable data pipelines with polars and dataframely | — |
| Yannik Tausch | PyCon DE & PyData 2026 | sponsor | Kickstart Coding at Scale: How Project Template Automation Unlocks Developer Productivity | — |
| Daniel Elsner | PyCon DE & PyData 2025 | sponsor | Dataframely — A declarative, 🐻❄️-native data frame validation library | — |
| Martin Stancsics | PyCon DE & PyData 2025 | talk | High-performance dataframe-agnostic GLMs with glum | — |
| Kevin Klein | PyData Paris 2024 | talk | Catering Causal Inference: An Introduction to 'metalearners', a Flexible MetaLearner Library in Python | — |
| Uwe L. Korn | PyCon DE & PyData Berlin 2024 | talk | Going beyond Parquet's default settings – be surprised what you can get | Slides |
| Kevin Klein | PyData Amsterdam 2023 | talk | Causal Inference Libraries: What They Do, What I'd Like Them To Do | — |
| Jakub Bachurski | PyCon DE & PyData Berlin 2023 | sponsor | Have your cake and eat it too: Rapid model development and stable, high-performance deployments | — |
| Pavel Zwerschke | PyCon DE & PyData Berlin 2023 | talk | Shrinking gigabyte sized scikit-learn models for deployment | — |
| Christian Bourjau | PyCon DE & PyData Berlin 2022 | talk | Making Machine Learning Applications Fast and Simple with ONNX | — |
| Jan-Benedikt Jagusch | PyCon DE & PyData Berlin 2022 | talk | Making Machine Learning Applications Fast and Simple with ONNX | — |
| Maximilian Eber | PyCon DE & PyData Berlin 2019 | talk | Leveraging ML to obtain fine-grained (yet reliable) causal estimates from A/B tests and experiments | — |
| Uwe Korn | Berlin Buzzwords 2019 | talk | Taming the language border in data analytics and science with Apache Arrow | Slides · Video |