Description

Lead the development and optimization of forecasting machine learning models to improve accuracy and scalability.

Responsibilities

• Build and manage end-to-end ML models and pipelines for forecasting.
• Develop, maintain, and optimize forecasting models to improve accuracy.
• Collaborate with data scientists and engineers to translate specifications into scalable, maintainable solutions.
• Research state-of-the-art machine learning technologies to integrate into existing workflows.
• Monitor and analyze ML model performance and data accuracy.

Required Skills

• 8+ years of experience in data science, including 3 years building forecasting solutions.
• Expertise in Python, Spark, and SQL.
• Hands-on experience with large-volume data and PySpark environments.
• Proficiency with forecasting algorithms: Time Series (Arima, Prophet), ML (GLM, XGBoost), Hierarchical models (Top Down, Bottom Up), DL (Seq2Seq, Transformer), and Ensemble methods.
• Experience with Git, Bitbucket, Airflow, MLflow, Kubeflow, GCP Vertex AI, or Databricks.
• Knowledge of large-scale data processing and distributed systems like Hadoop and Spark.
• Experience debugging and fine-tuning applications in distributed computing environments.

Key Skills

Education

Any Gradute