Data Scientist enhancing merchandising and inventory performance using machine learning techniques at Nordstrom. Collaborating with teams to develop data - driven solutions for better customer experiences.
About the role
- Data Scientist at Topaz leading advanced ML models for fraud prevention and ML pipeline automation, ensuring scalable and sustainable applications in production.
Responsibilities
- Lead the development and deployment of advanced models that drive large-scale fraud prevention.
- Develop and maintain automated ML pipelines (CI/CD) for model training, validation and deployment.
- Adapt data transformation pipelines for machine learning model inference.
- Ensure scalability and availability of ML applications in production environments.
- Collaborate with Data Scientists to build robust and scalable pipelines.
- Implement continuous monitoring systems for model performance in production.
- Identify and mitigate performance degradation, data drift and concept drift.
- Establish alerts and dashboards to track critical metrics.
- Implement automated retraining strategies and model versioning.
- Update models with new data while maintaining traceability and governance.
- Implement explainability techniques (XAI) to ensure transparency and regulatory compliance.
- Optimize infrastructure costs and processing time.
- Explore and evaluate new technologies, frameworks and MLOps tools.
- Contribute to defining best practices, technical standards and team documentation.
- Stay up to date with trends and innovations in Machine Learning Operations.
Requirements
- Experience in software development
- Experience working with data in MLOps or Data Science-related activities
- Proficiency in Python for developing robust applications
- Code versioning with Git
- Solid knowledge of data structures, algorithms and design patterns
- Data manipulation with NumPy and Pandas
- Model development with Scikit-Learn and TensorFlow
- Unit testing with pytest
- Load testing with Locust
- Hands-on experience with AWS (EC2, S3, Lambda, ECR, ECS/EKS)
- Containerization with Docker
- Orchestration with Kubernetes
- Infrastructure as Code with Terraform
- Experience with GitLab CI/CD for pipeline automation
- Experience with NoSQL databases (MongoDB, DocumentDB, DynamoDB)
- ORM model development for relational databases
- AWS certifications (Solutions Architect, Machine Learning Specialty) are a plus
- Experience with MLOps tools (MLflow, Kubeflow, Airflow) is a plus
- Knowledge of Feature Stores and Model Registry is a plus
- Experience with model monitoring frameworks is a plus
- Knowledge of data security and governance is a plus
- Open-source contributions are a plus.
Benefits
- Health & Well-being: Because we care about our teams, we offer various health plans focused on promoting well-being across the organization.
- Personal and professional development: We are constantly evolving. We provide environments, programs and policies that ensure you have the space and opportunities needed for growth 🎓 Career path.
- Flexibility and time off: Here you'll find the time necessary to recharge, and you can enjoy a day off on your birthday 🥳 Hybrid work arrangement.
- Partnerships and discounts: We offer various partner agreements and discounts.
Job title
Job type
Experience level
Salary
Not specified
Degree requirement
No Education Requirement
Tech skills
Location requirements
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