Back
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Tier-1 brand, mid-level ML role, common title, and metro location create high competition.
Core ML and time-series skills are transferable, but forecasting domain knowledge increases fit sensitivity.
Multiple mandatory filters (5+ years, forecasting, GCP, production ML) make shortlisting highly strict.
Design and deploy statistical and machine learning models for demand forecasting, capacity planning, and trend analysis.
Own the end-to-end model lifecycle including development, backtesting, production deployment, monitoring, and retraining on Google Cloud Platform.
Collaborate with cross-functional teams to translate model outputs into actionable business insights and ensure scalable data engineering support.
5+ years of experience in data science, applied statistics, or machine learning.
Proficiency in Python and data science/ML libraries (e.g., pandas, NumPy, scikit-learn, TensorFlow/PyTorch).
Experience with time series analysis, probabilistic forecasting, and production ML model deployment.
Experience working on Google Cloud Platform (GCP) and handling large datasets with SQL and Python.
Experienced in building both statistical forecasting models and advanced machine learning models for business applications.
Comfortable managing full model lifecycle and MLOps in cloud environments, especially GCP.
Able to effectively communicate technical modeling concepts and results to both technical and non-technical stakeholders across teams.