Description

Lead the development of probabilistic and deep learning models for demand forecasting, owning the end-to-end analytics workflow from data extraction to model deployment.

This role is on-site.

Responsibilities

• Build and evaluate time series forecasting models using machine learning and deep learning approaches.
• Implement probabilistic models using Bayesian statistics to quantify uncertainty in demand forecasts.
• Design supervised and unsupervised machine learning workflows in Python, including feature engineering and validation.
• Develop deep learning architectures using PyTorch or TensorFlow for complex forecasting tasks.
• Execute SQL-based data extraction and analytics workflows to support modeling and reporting.

Required Skills

• 12-15 years of experience in data science, with a focus on forecasting.
• Strong proficiency in Python for data science and machine learning.
• Hands-on experience with PyMC for Bayesian modeling and programming.
• Expertise in deep learning frameworks, specifically PyTorch or TensorFlow.
• Solid foundation in time series analysis and demand forecasting techniques.
• Advanced SQL skills for data extraction and analytics.
• Experience with Scikit-learn for traditional machine learning workflows.

Preferred Skills

• Experience with MS SQL Server in production environments.
• Background in electrical, computer science, or industrial engineering.

Key Skills

Education

Any Graduate