Scientifique Principale des Données-Ingénieur - Staff Data Scientist - Eng
Tasks
- Analyze workforce data to identify product opportunities
- Automate model retraining triggers
- Build RAG systems
- Build end-to-end machine learning pipelines
- Communicate model results and infrastructure tradeoffs
- Convert business needs into scalable machine learning solutions
- Define ML engineering evaluation and deployment standards
- Deploy models with validation monitoring and iteration
- Design agentic workflows
- Design low latency high availability model infrastructure
- Detect data drift and performance degradation
- Develop and deploy Generative AI systems
- Implement CI CD for machine learning including canary deployments
- Implement model evaluation frameworks
- Lead end to end technical decisions for ML initiatives
- Mentor data science professionals through code and model reviews
- Optimize training and inference cost using mixed precision quantization distillation and batching
- Train and fine tune transformer language models
- Write production modular code and data model pipelines
Perks/Benefits
- N/A
Skills/Tech-stack
AWS SageMaker | Airflow | Azure ML | CI/CD | Canary Deployment | DPO | Data Drift | Deep learning | Distillation | Docker | Docker N/A | Efficient Inference | Generative AI | GitHub Actions | Google BigQuery | Hugging Face | Hugging Face Transformers | Inference Batching | Knowledge Distillation | Kubeflow | Kubernetes | Kubernetes N/A | Langfuse | Langsmith | Language Processing | LoRA | MLflow | Machine Learning | Model Deployment | Model Monitoring | Model Pruning | Model Quantization | N A | Natural Language | Natural Language Processing | Pandas | Pulumi | PySpark | PyTorch | Python | QLoRA | Quantization | RAG | RLHF | SQL | Scikit-learn | Tensorflow N/A | Terraform | Transformer Models | Vertex AI | Weights and Biases | Weights and Biases N/A
Education
Bachelor of Engineering | Bachelor of Science | Master of Science | PhD
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