Data Scientist
Tasks
- Automate data ingestion pipelines
- Build fraud detection and prevention models
- Build machine learning models
- Collaborate with model validation compliance and risk teams
- Conduct AML credit risk and fraud analytics
- Design and deploy agentic AI workflows
- Develop credit scoring models
- Develop predictive models
- Ensure model versioning change management and reproducibility
- Evaluate and select LLM and RAG architecture
- Execute ad-hoc analyses
- Identify escalate mitigate and resolve risk conduct and compliance matters
- Monitor false positive and false negative rates
- Monitor model performance drift and stability
- Perform exploratory data analysis
- Support model governance lifecycle
- Validate model inputs assumptions and effectiveness
- Visualize data and present insights
Perks/Benefits
- Employee assistance programme
- Flexible working
- Learning and development courses
- Life insurance
- Medical insurance
- Parental leave
- Retirement savings plan
- Sabbatical
- Volunteering leave
- Wellbeing support programs
Skills/Tech-stack
AML analytics | AWS | Agentic AI | Anomaly Detection | Clustering | Credit Risk | Credit risk modeling | Data Analysis | Data Modeling | Data Pipelines | Data Studio | Data Visualization | Decision Trees | Deep learning | Exploratory Data Analysis | Feature Selection | Fraud Detection | Google BigQuery | Google Bigtable | Google Data Studio | Google Dataflow | Language Models | Language Processing | Large Language Models | Machine Learning | Model Governance | Model Monitoring | Natural Language | Natural Language Processing | Neural Networks | Python | R | Retrieval-Augmented Generation | Risk Modeling | Statistical modeling | Tableau | Transaction monitoring
Education
Roles
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