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North is building a next-generation cloud, AI, and data spend management platform. The company provides real-time visibility, control, and automation over digital asset finances, helping companies grow efficiently without wasting time or money. You'll join the Services team as a Software Engineer focused on time series forecasting.
Forecasting is foundational to North's platform: projected spend, budgets, and commitment recommendations all depend on accurate forecasts of where customer usage is headed. When forecasts are off, customers feel the impact through missed savings or unused commitments.
In this role, you'll own the models behind North's internal forecasting service and shape how it evolves. You'll work with teammates to deliver forecasts across North's features, collaborating closely with data scientists and product teams who rely on forecast results to make decisions.
Key responsibilities include:
- Building and improving time series models for projected spend, budgets, and commitment recommendations across customers with varying sizes and usage patterns
- Partnering with Services teammates to deploy models into production as a reliable shared service with clear interfaces
- Shaping the forecasting service architecture, including model training, versioning, evaluation, and serving
- Improving evaluation, backtesting, and monitoring of forecast accuracy over time
- Building proactive detection and alerting for unusual forecasts and model drift
- Quantifying forecast uncertainty through prediction intervals so downstream features can account for it
- Collaborating with data scientists to translate forecast results into actionable findings for product and business teams
- Working with Commitments, Visibility, and Data teams to understand feature-specific forecasting needs
The company is hiring without a fixed level and will level you based on experience and work style using their engineering ladder.
Work Setup: Hybrid role based in Brooklyn (Dumbo office). Team members typically work in-office 2–3 days per week, or fully remote within the US.
Requirements:
- 4+ years of software or machine learning engineering experience, including shipping time series models to production and owning them post-launch
- Deep experience with time series forecasting across classical statistical methods and machine learning approaches, with judgment about when each applies
- Experience with time-aware validation, backtesting, and forecast accuracy metrics, and understanding of their tradeoffs
- Experience monitoring production models, including drift detection, anomaly detection, and retraining
- Fluency in Python and SQL, and experience working with large datasets
- Experience collaborating with data scientists and explaining model behavior and uncertainty to non-engineering stakeholders
- Pragmatic, informed use of AI coding tools
Nice to Have:
- Data science background or analysis work alongside ML engineering
- Experience with probabilistic forecasting
- Experience forecasting usage, demand, or spend across many accounts with different patterns
- Experience with cloud usage, billing, or pricing data
- Experience with AWS ML services
- Familiarity with FinOps concepts
