Description

You will own the development, deployment, and optimization of forecasting models within a data science context.

Responsibilities

• Build, train, and deploy time-series forecasting models using techniques like XGBoost, Random Forest, ARIMA, and Prophet.
• Operationalize model pipelines using Google Cloud Platform (GCP) services or equivalent cloud technologies.
• Develop scalable data processing workflows using Python, PySpark, and SQL on large datasets.
• Design and implement data anomaly detection systems and build automated alerting frameworks for forecast issues.
• Maintain accuracy measurement frameworks (e.g., MAPE, RMSE) and continuously optimize model performance.

Required Skills

• 5+ years of experience as a Data Scientist with hands-on model development.
• Strong programming skills in Python, including PySpark and standard data science libraries (Pandas, NumPy, scikit-learn).
• Expertise in time-series forecasting, regression models, and ensemble methods.
• Proficiency with SQL and experience working with large-scale datasets.
• Hands-on experience with GCP services (BigQuery, Cloud Composer, Dataproc, Cloud Run) or comparable cloud platforms.
• Ability to diagnose data issues, perform anomaly analysis, and build automated alerting solutions.
• Bachelor's degree in Data Science, Computer Science, Statistics, Mathematics, or related field.

Preferred Skills

• Experience in retail, supply chain, inventory, or demand forecasting systems.
• Exposure to MLOps practices, including CI/CD and model monitoring.

Key Skills

Education

Bachelor's degree