Data Scientist
Summary
Design, build, and deploy machine learning and statistical models for fraud detection, loan risk assessment, and customer scoring at a digital bank using Python, TensorFlow/PyTorch, and cloud ML platforms like AWS SageMaker.
You will design, build, and deploy machine learning and statistical models for fraud detection, transaction monitoring, loan risk assessment, customer risk scoring, and sales and lead generation. You will build experimentation frameworks, analyze data, engineer features, collaborate with stakeholders, document your work, and apply advances in machine learning and generative AI.
Responsibilities
- Design build and deploy machine learning and statistical models
- Build and operate experimentation and evaluation frameworks
- Analyze data and engineer features to improve model performance
- Define requirements with data engineers analysts and business stakeholders
- Translate technical insights into business decisions
- Document models methodologies and results
- Apply machine learning and generative AI advances
Requirements
- Experience developing and deploying machine learning models in production
- Proficiency in Python and relevant machine learning libraries such as scikit-learn TensorFlow and PyTorch
- Experience with cloud-based machine learning platforms such as AWS SageMaker or Kubeflow
- Strong grounding in machine learning statistical modeling and data analysis
- Experience in financial services or another regulated industry is a plus
- Strong problem-solving and communication skills
Benefits
- State-of-the-art computer monitor mouse and keyboard
- Pension
- Health insurance
- Enhanced parental leave