Description

- Design, develop, and deploy end-to-end predictive analytics and machine learning solutions using structured enterprise datasets.

- Build advanced forecasting models to estimate operational workloads and identify drivers of engineering complexity.

- Apply a wide range of ML techniques, including regression, classification, clustering, ensemble methods, deep learning, and time-series forecasting.

- Conduct Exploratory Data Analysis (EDA) and statistical modeling to uncover patterns, trends, and anomalies in high-dimensional datasets.

- Develop and optimize scalable ML pipelines for data ingestion, feature engineering, model training, inference, and continuous monitoring.

- Write and optimize complex SQL queries within Google BigQuery to manipulate large-scale enterprise data.

- Deploy and manage AI/ML applications on Google Cloud Platform (GCP) utilizing Vertex AI and cloud-native services.

- Build and maintain scalable APIs and backend services using Python frameworks such as FastAPI, Flask, or Django.

- Implement MLOps best practices to ensure model reproducibility, version control, and performance monitoring in production.

- Collaborate with cross-functional teams (Engineering, Validation, Analytics) to translate complex business requirements into technical specifications.

- Develop interactive dashboards and visualization layers to communicate forecasting insights and engineering trends to stakeholders.

- Present technical insights and strategic recommendations to both technical audiences and executive leadership

Key Skills

Education

Any Gradute