Senior Data Scientist

Overview: • We are seeking a Senior Data Scientist / Machine Learning Engineer to join a high-impact team focused on building large-scale forecasting and planning solutions. This role will contribute to the development of advanced models that support long-term decision-making across complex systems, leveraging massive datasets and cloud-native architectures. • You will work on a collaborative, cross-functional team delivering production-grade machine learning solutions that operate at scale. This is an opportunity to build and deploy forecasting models that directly influence strategic planning and infrastructure investment decisions. • This role is ideal for someone who thrives in a fast-paced environment, enjoys solving ambiguous problems, and has a strong foundation in both modeling and production ML systems. Required • Strong experience with Python for data science and machine learning • Hands-on experience with MLflow or similar tools for experiment tracking and model lifecycle management • Experience working in AWS environments, including services such as: S3, Lambda, Athena, EMR, SageMaker, Airflow (or similar orchestration tool) • Proven experience developing time-series or forecasting models, ideally at scale • Experience building scalable machine learning systems that process large datasets or run across millions of records • Strong ability to work in cross-functional teams and contribute to complex, production-grade systems • Ability to independently research problems and propose effective modeling solutions Responsibilities • Develop and deploy time-series and forecasting models at scale, including long-horizon and high-volume predictions • Design and build scalable machine learning pipelines capable of processing large datasets and running across millions of records • Implement and manage end-to-end MLOps workflows including experiment tracking, model versioning, and monitoring • Collaborate with data engineers, software engineers, and product stakeholders to ensure seamless integration of models into production systems • Work with cloud-native tools to build and optimize data pipelines and model training/inference workflows • Research, prototype, and implement new modeling approaches, including probabilistic and advanced forecasting techniques • Ensure model performance, reliability, and scalability in production environments • Contribute to system architecture and help drive best practices in machine learning engineering