About the Role
The candidate will lead the development of advanced forecasting models, integrate machine learning pipelines into production systems, and collaborate with cross-functional teams to deliver accurate, real-time predictions.
Responsibilities
- Design and implement scalable machine learning models for time-series forecasting
- Develop robust data pipelines to support model training and inference
- Optimize model performance through feature engineering and hyperparameter tuning
- Collaborate with data scientists and software engineers to deploy models in production
- Monitor model accuracy and retrain models based on new data inputs
- Evaluate and integrate third-party forecasting libraries and tools
- Ensure models comply with data privacy and security standards
- Troubleshoot and resolve issues in model deployment and serving infrastructure
- Contribute to architectural decisions for the forecasting platform
- Work closely with product teams to understand forecasting requirements
- Improve model interpretability and explainability for stakeholders
- Support A/B testing of forecasting algorithms
- Document model design, training processes, and performance metrics
- Stay current with advancements in machine learning and forecasting techniques
- Mentor junior team members on best practices in ML engineering
Nice to Have
- Experience with probabilistic forecasting or uncertainty quantification
- Background in econometrics or demand forecasting
- Contributions to open-source machine learning projects
- Prior work in high-scale, real-time prediction systems
- Familiarity with monitoring and observability tools for ML systems
- Knowledge of automated machine learning frameworks
- Experience mentoring engineers in a technical leadership role
- Publication record in machine learning or data science venues
Compensation
Competitive salary with performance-based incentives
Work Arrangement
Hybrid work model with flexibility for remote and office presence
Team
Collaborative data science and engineering team focused on predictive systems
Technology Stack
- Primary languages: Python, SQL
- Frameworks: PyTorch, TensorFlow, scikit-learn
- Cloud infrastructure: Google Cloud Platform
- Orchestration: Kubernetes, Docker
- Data pipelines: Apache Airflow, BigQuery
Team Mission
- Build accurate, scalable forecasting systems
- Enable data-driven decision-making across business units
- Continuously improve model reliability and performance
Available for qualified candidates requiring work authorization
