Tasks:
- * Apply experimentation and forecasting
- * Automate model monitoring and retraining
- * Build predictive features from large datasets
- * Communicate model results
- * Define success measures with stakeholders
- * Deploy and maintain production machine learning models
- * Detect model drift
- * Develop and evaluate classification and regression models
- * Document technical designs
- * Improve data quality and feature pipelines
- * Review solutions and improve coding standards
- * Translate business problems into machine-learning objectives
Perks/Benefits:
- + 24/7 medical support
- + Annual loyalty leave
- + Financial wellbeing services
- + Hybrid working
- + Mental health support
- + Paid parental leave
- + Wellbeing programs
- + Wellness initiatives
Skills/Tech stack required:
[Agentic AI] [AWS SageMaker] [Azure ML] [CI/CD] [Classification] [Cloud Machine Learning] [Customer Propensity] [Customer propensity modelling] [Databricks] [Data Pipelines] [Data Quality] [Drift Detection] [Experimentation] [Feature Engineering] [Feature Stores] [Forecasting] [Generative AI] [Language Models] [Large Language Models] [Machine Learning] [MLOps] [Model Evaluation] [Model Monitoring] [Model retraining] [Personalisation] [Predictive Analytics] [Probability] [Production Quality] [Production-Quality Code] [Propensity modelling] [Python] [Quality code] [Recommendation Systems] [Regression] [Statistics]
Educational requirements:
N/A
Role(s):
[Applied Scientist] [Data Scientist] [Engineer] [Learning Engineer] [Machine Learning Engineer] [Scientist] [Senior Data Scientist]