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Sybilion Tech Stack
Demand forecasting platform for supply chain and procurement
Sybilion builds demand forecasting models for supply chains using Python, Prophet, and Postgres. The tech stack is data-science-heavy (pandas, NumPy, Prophet) with enterprise deployment infrastructure (Kubernetes, Docker, Terraform), suggesting models that move from experimentation to production at scale. The hiring mix—3 data roles, 2 sales, and 1 each in engineering, design, and product—reflects a company pivoting from model-building toward repeatability: projects span forecasting development, operationalization into workflows, and GTM engine construction, while pain points center on data quality and forecast adoption rather than algorithm sophistication.
Tech Stack 32 technologies
Core StackPython pandas NumPy Kubernetes Linux Docker Terraform Ansible Helm Prometheus Grafana Elasticsearch Loki PostgreSQL FastAPI Flask Django Prophet Git Elasticsearch Fluentd Kibana Vault Sealed Secrets pip Poetry conda Ubuntu Red Hat Enterprise Linux Excel Google Sheets SQL+2 more
What Sybilion Is Building
◆Challenges
- Uncertain forecasting
- Messy market data
- Decision support for procurement
- Messy external signals
- Forecast adoption challenge
- Turn messy data into actionable forecasts
- Reduce inventory risk
- Improve procurement decision-making
- Complex industrial supply chains
- Volatility in supply chains
▲Active Projects
- Forecasting model development
- Model operationalisation into decision workflows
- Model-led pocs for clients
- Forecast delivery for german-speaking customers
- Adoption milestone tracking
- Model-led pocs and client delivery
- Enterprise pipeline creation
- Gtm engine building
- Building repeatable sales processes
- Build the gtm engine
Hiring Activity
8 roles · 5 in 30d
Department
Seniority
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