Summary:

We are seeking a highly skilled and experienced Senior Data Scientist to join our team. The ideal candidate will be a hands-on expert in developing and deploying deep learning-based forecasting models at scale. You will be responsible for the entire model lifecycle, from ideation and data preparation to model training, deployment, and monitoring in a production environment. This role requires a deep understanding of time series analysis, a passion for building innovative solutions, and the ability to work with large, complex datasets.

Responsibilities:

• Design, develop, and implement state-of-the-art deep learning forecasting models to solve critical business problems.
• Lead the entire model development lifecycle, including data cleaning, feature engineering, model selection, training, and validation.
• Build scalable and robust forecasting solutions capable of handling massive, high-dimensional time series data.
• Select and apply appropriate deep learning architectures for time series, such as LSTMs, GRUs, and Transformers.
• Utilize and evaluate various forecasting libraries and frameworks like Neural Forecast , GluonTS , TSAI , TSLib , Merlion , PyTorch Forecasting , Darts , Orbit and others.
• Collaborate with data engineers to build and maintain the necessary data pipelines and infrastructure for large-scale model training and deployment.
• Perform rigorous model evaluation using appropriate time series-specific techniques like walk-forward validation and backtesting.
• Communicate complex technical concepts and model performance to both technical and non-technical stakeholders.
• Stay up-to-date with the latest research and advancements in deep learning for time series forecasting.

Required Qualifications:

Preferred Qualifications:

• Advanced degree (M.S. or Ph.D.) in a quantitative field such as Computer Science, Statistics, Mathematics, or a related discipline.
• 3 years experience in the forecasting domain.