Role & Responsibilities Model Development and Optimization:
Collaborate with cross-functional teams to deploy ML models for strong use cases, such as recommendation systems, customer segmentations, and demand forecasting
Collaborate with data analysts and engineers to implement scalable ML solutions
Deployment and Integration:
Deploy machine learning models into production environments using Kubernetes, and cloud platforms (GCP, AWS)
Establish and manage MLOps practices, including model monitoring, versioning, and retraining strategies
Automate model training, testing, and deployment processes to improve efficiency and reliability and maintain CI/CD pipelines for model tracking
Building Analytics Capabilities:
Be a custodian of data by cleaning, structuring, and maintaining our datasets, and keeping them ready for visualization or modeling purposes
Monitoring the impact of implemented use cases and communicating to the organization the value generated
Building business team capabilities and awareness around analytics and making sure they adopt the use cases implemented.
Requirements:
Proficiency in Python, SQLExperience with ML frameworks (e.g., TensorFlow, PyTorch, Scikit-learn)
Strong Knowledge of deployment tools (e.g., Docker, Kubernetes, cloud platforms) and MLOps
Extensive knowledge of cloud-serverless technologies such as AWS Lambda, Google Cloud Functions, or Azure Functions
Strong familiarity with GCP is a plus deploying ML models/ user cases in the E-commerce industry is a plus. Strong background in statistical analysis, data visualization, and hypothesis testing
