Applied Machine Learning Lead
Tasks
- Collaborate with product and engineering teams
- Communicate insights to technical and non-technical stakeholders
- Design build deploy and maintain production machine learning models
- Develop reusable machine learning capabilities and workflows
- Drive machine learning platform and team practice improvements
- Ensure governance and policy adherence
- Ensure statistically sound and commercially meaningful results
- Establish model review and documentation standards
- Lead and manage data science team
- Lead applied machine learning initiatives
- Manage data science backlog and roadmap
- Monitor model performance and data drift
- Own experimentation lifecycle
- Perform advanced commercial analytics
- Provide technical leadership for modelling and feature engineering
- Review validate and challenge models and experiments
- Translate business problems into measurable initiatives
Perks/Benefits
- N/A
Skills/Tech-stack
A/B | A/B Testing | AWS | Apache Spark | Azure | B testing | Causal Inference | Cloud Computing | Cloud platform | Code review | Data Drift | Databricks | Deployment Workflows | Documentation | Experimentation | Feature Engineering | Google Cloud | Google Cloud Platform | Hypothesis Testing | Lifecycle Management | MLOps | Machine Learning | Maintainability | Model Lifecycle | Model Lifecycle Management | Model Monitoring | Model Validation | Observability | Predictive Analytics | Production Readiness | Python | Retraining | SQL | Statistical modeling | Testing | Version control
Education
N/A
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