Staff Data Scientist - Forecasting
Company: Veho
Location: Location not specified (Remote)
Type: Full-time
Remote: Yes
Posted: 2026-06-20
About this role
About The Role
As a Staff Data Scientist for Forecasting, you will be the technical leader responsible for building and owning the sophisticated models that power Veho's Sales and Operations Planning (S&OP) process. Your forecasts will be the backbone of our strategic and operational decision-making across our network: it ensures we understand what volume we should expect in our network, how it will flow from injection to delivery, and where our bottlenecks will be.
You’ll be directly embedded in a team of talented software and data engineers and partner closely with leaders in Operations, Finance, and Product to translate their business challenges into scalable forecasting solutions. You will be responsible for operationalizing the models you build in the production systems that drive Veho's growth and efficiency.
What You’ll Do
- Create the forecasting algorithms that drive our Sales &Operations Planning process.
- Building reliable, efficient, and scalable models for our AI/ML capabilities
- Creating robust data pipelines to feed analyses and models
- Analyze and evaluate the impact and effectiveness of models in production systems
- Driving science roadmap for forecasting for Veho alongside cross-functional partners.
- Leverage AI-native development workflows and tooling (Claude Code, Cursor, Copilot, etc.) to accelerate throughput
- Mentor other Data Scientists and drive team-wide improvements to our data science practices.
What You Bring
- Bachelor’s Degree plus 8 years of experience in Data Science, Data Engineering or a similar degree, or Master’s Degree plus 5 years of experience, or Phd plus 3 years of experience
- Deep expertise in a variety of time series forecasting methods (e.g., ARIMA, Prophet, exponential smoothing, LSTMs, Transformer-based models) and a strong understanding of their theoretical underpinnings.
- Experience in developing and testing forecasting systems in a supply chain context, combining statistical forecas...