Lead/Principal Data Scientist

Job Title: Lead/Principal Data Scientist - Forecasting

Location: Fully Remote

Compensation: $180,000 - $250,000 base, bonus + equity (based on leveling)

Role: Lead/Principal Data Scientist – Forecasting

A well-funded, fast-growing B2B SaaS company is scaling its Forecasting Data Science team. The mission: build and productionize large-scale forecasting systems that directly influence how customers price, plan, and operate in dynamic markets. These roles sit at the core of the platform, ensuring forecasts are not just accurate but actionable across high-volume, multi-market environments.

Why This Role Matters

Forecasting is central to how this business and its customers make strategic decisions. These systems are being rebuilt for scale, accuracy, and speed, creating a rare opportunity to tackle high-dimensional forecasting challenges with direct business impact.

You’ll be working in a supportive environment with modern MLOps foundations, cross-functional collaboration, and the mandate to push the boundaries of applied forecasting science.

What You’ll Do

• Design and scale forecasting models across thousands of entities (locations, SKUs, or comparable dimensions)
• Drive the full lifecycle from research through deployment, ensuring models operate reliably in production
• Collaborate with product, data, and engineering teams to align forecasts with business needs
• Contribute to the roadmap for advanced areas such as reinforcement learning, causal inference, and decision optimization
• Mentor junior data scientists and set technical direction depending on level (Lead vs. Principal)

Tech Stack

• Languages/Frameworks: Python, PyTorch, TensorFlow, SQL, scikit-learn
• Infra: Cloud-native ML platforms (AWS, GCP, or equivalent), MLflow, Airflow
• Modeling: Time series forecasting (hierarchical, Bayesian, deep learning), demand elasticity, causal inference methods
• Deployment: Batch and real-time APIs integrated into production systems

Ideal Candidate

• Experienced data scientist with strong background in forecasting, Bayesian modeling, or large-scale machine learning
• Skilled at taking models from prototype to production in complex data environments
• Comfortable balancing research innovation with engineering pragmatism
• For Principal: proven experience setting technical vision and influencing cross-functional strategy
• For Lead: demonstrated leadership and mentorship while staying hands-on technically

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