Lead Applied Scientist
Tasks
- Apply model explainability and validation
- Conduct prompt engineering and retrieval augmented generation
- Define evaluation metrics and acceptance thresholds
- Develop generative AI and agentic AI workflows
- Escalate technical decisions
- Evaluate and validate AI model accuracy
- Frame business problems for AI solutions
- Lead AI and ML solution design
- Lead experimentation model development and experimentation
- Monitor models for drift in production
- Perform responsible AI bias and fairness testing
- Review code and mentor team members
- Set experimentation evaluation and documentation standards
- Write production-quality code
Perks/Benefits
- N/A
Skills/Tech-stack
Agentic AI | Apache Spark | Bias and fairness | Bias and fairness testing | Databricks | Deep learning | Experiment design | Explainability | Fairness testing | Fine Tuning | Generative AI | Language Models | Large Language Models | Linear Algebra | MLflow | Machine Learning | Model Evaluation | Model Monitoring | Model Validation | Model risk | Multi-step agents | NumPy | Optimization | Pandas | Probability | Prompt engineering | Python | Retrieval-Augmented Generation | Scikit-learn | Statistical Analysis | Tool use
Education
Bachelor of Science | Master of Engineering | Master of Science | PhD
Roles
Applied Scientist | Data Scientist | Lead | Lead Applied Scientist | Scientist
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