Senior Data Scientist - ML, Python

Key Responsibilities

Pipeline Development & Automation: Design and implement automated ML pipelines for model training, testing, deployment, and monitoring. Model Deployment & Monitoring: Deploy AI/ML models into production environments using containerization and orchestration tools; ensure performance and reliability. Cloud Infrastructure Management: Configure and optimize cloud resources (AWS SageMaker, S3, Bedrock) for scalable ML workflows. Data Integration: Collaborate with Data scientist to streamline data ingestion and transformation for model readiness. CI/CD for ML: Implement continuous integration and delivery practices tailored for ML workflows. Performance Optimization: Monitor model performance, retrain as needed, and manage versioning for reproducibility. Collaboration: Work closely with Data Scientists to translate experimental models into production-ready solutions.

Requirements

MLOps Expertise: Strong knowledge of ML lifecycle management, pipeline automation, and monitoring tools. Cloud Platforms: Hands-on experience with AWS (SageMaker, Lambda, ECS/EKS), Snowflake, and related services. Programming: Proficiency in Python; familiarity with ML frameworks (PyTorch, TensorFlow). Containerization & Orchestration: Experience with Docker and Kubernetes for scalable deployments. CI/CD Tools: Knowledge of GitHub Actions, Jenkins, or similar tools for automated workflows. Data Engineering: Ability to work with SQL and integrate data from multiple sources.

Key Responsibilities

1. Design And Implement Advanced Machine Learning Algorithms Using Tensorflow And Pytorch To Extract Insights From Large Datasets, Delivering Customized Analytical Reports Tailored To The Needs Of Colleagues, Clients, And Stakeholders.
2. Leverage Python And Sql To Develop And Optimize Data Models That Address Specific Organizational Challenges, Ensuring Effective Integration And Management Of Data.
3. Analyze And Mine Large Datasets To Identify Trends And Patterns, Utilizing Advanced Analytics Techniques To Interpret Findings And Provide Actionable Recommendations Based On Experimental Results.
4. Collaborate With Cross-Functional Teams To Identify Opportunities For Utilizing Data Insights To Formulate Strategic Business Solutions That Enhance Operational Efficiency And Product Offerings.
5. Create Comprehensive Visualizations And Reports Using Data Visualization Tools To Communicate Complex Analysis And Results Clearly, Enabling Informed Decision-Making For Customers And Stakeholders.

Skill Requirements