Senior Predictive Liability Analytics Lead
21650 Oxnard Street, Woodland Hills, CA, United States
USD 190K-210K Senior-level Full Time
Tasks
- Build deep learning models
- Build predictive models for policyholder behavior
- Build training and scoring pipelines
- Conduct A B and causal inference experiments
- Create NLP and LLM based features
- Design GLM and GAM models
- Develop tree ensemble models
- Generate versioned results tables
- Implement rag pipelines
- Implement survival and hazard models
- Integrate model outputs into projection engines
- Perform scenario stress and sensitivity runs
- Prepare model documentation and explainability
- Serve models via batch and API
- Set up model monitoring dashboards
- Track experiments with MLflow
Perks/Benefits
- 401k match
- Dental insurance
- Employee assistance program
- Health insurance
- Mental health support
- Paid time off
- Remote work options
- Vision insurance
- Volunteer time off
- Wellness initiatives
Skills/Tech-stack
A/B | A/B Testing | AWS | Azure | B testing | CI/CD | CatBoost | Causal Inference | Cox Proportional Hazards | Data Drift | Databricks | Deep learning | Docker | Embeddings | Evolutionary Algorithms | Genetic Algorithms | Git | Hazard models | LightGBM | MLflow | Model Monitoring | NLP | NumPy | Pandas | Power BI | Prompt engineering | PyTorch | Python | R | RAG | SQL | Scikit-learn | Snowflake | Spark | Survival analysis | Tablue | TensorFlow | Transformers | Uplift Modeling | XGBoost
Education
Regions
Countries
States
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