Engineering Manager / Architect — Data Science & ML
Tasks
- Build GxP compliant ML pipelines
- Conduct code reviews and architectural reviews
- Conduct performance reviews
- Deploy models to production
- Design machine learning models
- Detect model performance drift
- Ensure model validation and audit trail compliance
- Ensure model versioning reproducibility and documentation
- Establish MLOps practices
- Evaluate model performance
- Implement model governance
- Ingest preprocess train and monitor data
- Integrate ML models into SaaS platform
- Manage and mentor ML engineers and data scientists
- Monitor experiments and production models
- Own platform technical roadmap
- Perform statistical analysis
- Recruit and interview ML talent
- Retrain models
- Serve as technical escalation point
- Set technical goals and OKRs
- Train and optimize deep learning models
- Translate business requirements into ML specifications
Perks/Benefits
- N/A
Skills/Tech-stack
21 CFR | 21 CFR Part 11 | API Design | API Integration | AWS | Azure | CFR Part 11 | Change Control | Cloud platform | Data Ingestion | Data Preprocessing | Data Science | Data Science Pipelines | Deep learning | Drift Detection | Experiment tracking | Google Cloud | Google Cloud Platform | GxP | MLOps | Machine Learning | Model Deployment | Model Serving | Model qualification | Model versioning | Part 11 | Production Monitoring | Quality analytics | Reproducibility | Scalability | Statistical modeling | Statistics
Education
Bachelor of Engineering | Bachelor of Science | Master of Science | PhD
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