Data Scientist - Forecasting & ML Systems

About SupplyWhy

SupplyWhy is building the future of autonomous supply chain planning. We deploy AI systems that replace fragile, spreadsheet-based processes with resilient, self-healing supply chains capable of making real-time decisions at enterprise scale. Starting with the automotive industry, we're tackling inefficiencies in one of the world's most complex supply chain ecosystems.

Our platform combines probabilistic forecasting, agentic AI, and deep supply chain domain expertise. We work with Fortune 500 manufacturers to transform how they plan, forecast, and respond to disruptions.

About the Role

As a Data Scientist on our Core ML team, you'll own and deliver key components of our forecasting and ML systems. This is hands-on technical work: time-series modeling, feature engineering, model productization, and MLOps. You'll work directly with the Lead DS to execute on our product roadmap for enterprise customers.

We're looking for someone who can operate independently - scoping work, making technical decisions, and delivering production-ready code with minimal oversight. You'll also help mentor junior team members as we grow.

What You Will Do

Own end-to-end development of time-series forecasting models (statistical and ML-based) for demand predictionDesign and implement feature engineering pipelines for structured and unstructured dataBuild anomaly detection systems for data quality and demand signal monitoringArchitect and deliver production-quality code: config-driven, testable, scalable Python modulesImplement model ensembling, hierarchical reconciliation, and automated model selectionDrive MLOps infrastructure: experiment tracking, CI/CD, model registryMake technical decisions on model architecture, data pipelines, and system designMentor junior team members and contribute to technical standardsCollaborate with the team using AI-assisted development tools (Cursor, Claude Code)

Must Have

Bachelor's or Master's in Computer Science, Statistics, Mathematics, or related quantitative field2-4 years of experience in applied ML/DS roles with production deploymentsStrong Python skills with experience writing production-quality, maintainable codeHands-on experience with time-series forecasting or demand predictionProficiency with ML frameworks (scikit-learn, CatBoost/XGBoost, or similar)Solid understanding of statistical concepts: probability distributions, hypothesis testing, regression, stationarityExperience with SQL and data manipulation at scale (pandas, numpy, SQL)Track record of delivering ML projects from prototype to productionAbility to work independently, make technical decisions, and communicate trade-offsComfortable in a remote-first environment with async collaboration

Nice to Have

Experience with probabilistic forecasting, quantile regression, or uncertainty estimationHands-on exposure to MLOps tools (MLflow, DVC, Airflow, Kubeflow, or similar)Background in supply chain, manufacturing, logistics, or enterprise operationsExperience with hierarchical/grouped time-series methods or forecast reconciliationFamiliarity with LLMs and AI-assisted development workflowsExperience mentoring junior engineers or data scientists

Why Join Us

Production ML at scale: Your models power forecasts for Fortune 500 manufacturersTechnical ownership: Own projects end-to-end, make architectural decisionsDepth over breadth: Specialize in time-series, forecasting, and ML systems - not generalist workAI-native workflow: Heavy use of AI tools (Claude, Cursor) - work at the frontier of modern developmentHigh-trust environment: We hire good people and give them autonomyShape the team: Early hire = influence on technical direction and culture as we growRemote-first: Flexible work from anywhere