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Data Scientist
2 weeks ago
Job Duties
- Thorough technical validation of code and documentation of ETL and data pipelines, predictive models, and their deployment.
- Provide validation findings and recommendationsto the modelers.
- Develop a championchallenger ML/Bayesian/DL/AI model as needed by the business.
- Support the Director, Model Risk Governance in documenting technical findings for presentation to validation groups and Company's executive.
- Oversee and ensure an endtoend ML/Bayesian/DL/AI model lifecycle is built in accordance with the company's model risk management framework and industry best practices.
- Assist pipeline automation and other risk analytics efforts.
- Advanced degree in highly quantitative field (Applied Statistics, Applied Mathematics, Applied Physics, Computer Science, Operational Research, Applied Finance, Econometrics, Actuarial, Engineering, etc). Ph.
- Minimum 1 year of work experience in ML/Bayesian/DL/AI model development and deployment. Experience in model risk, quantification and validation is required.
- Strong programming fundamentals and advanced knowledge in Python, Pandas, R, PySpark, SQL, and similar tools.
- Solid understanding of various ML/Bayesian/DL/AI algorithms and libraries, e.g., Scikitlearn, Spark MlLib, TensorFlow, Keras, PyTorch, RL etc.
- Experience with a data visualization tools, particularly PowerBI.
- Experience with AWS cloud services.
- Experience with containerization and orchestration tools, particularly Dockers and Kubernetes.
- Experience working with advanced Git Workflows (pull requests, code reviews, issues, and branching), Jira, and Confluence.
- Familiarity with any microservices such as Flask, Plumber, React, NodeJS, etc.
- Familiarity with Unix environment and scripting.
- Strong written and verbal communication skills enabling the translation of technical results into nontechnical explanations.
- Fast learner, proactive, resultsoriented, strong problemsolving attitude, and a team player.
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