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DataHow AG Tech Stack
AI-driven bioprocess modeling and optimization for pharmaceutical manufacturing
DataHow AG builds software for bioprocess development and optimization, targeting biopharma manufacturers. The stack—Python, PyTorch, scikit-learn, JAX, Julia, and Polars—reflects a research-heavy ML organization centered on time-series prediction and process modeling. Active projects span chromatography algorithms, multivariate forecasting, and AutoML pipelines, while the hiring mix (3 engineers, 1 research, 1 sales) and Portugal expansion signal early-stage scaling of both product and go-to-market.
Tech Stack 17 technologies
Core StackHubSpot Python PyTorch scikit-learn Go Rust Pandas NumPy TypeScript Kubernetes MongoDB Redis Julia JAX Polars SQL Azure
What DataHow AG Is Building
◆Challenges
- Crm ecosystem optimization
- High-quality customer data
- Pre-conference activity streamlining
▲Active Projects
- Crm process improvement initiative
- Lead nurturing campaign refinement
- Trade show and webinar support
- Production integration of machine learning algorithms
- Data transformation and model training pipelines in automl software
- Research and development of novel ml models for multivariate time-series prediction in biopharma
- Algorithmic backbone for chromatographic process unit
- Hybrid chromatographic models
- Algorithmic solution for core process unit in datahowlab
- Multivariate timeseries prediction models
Hiring Activity
6 roles · 5 in 30d
Department
Seniority
Company intelligence
