- Design develop and deploy production-grade machine learning models for financial and taxation use cases
- Build and maintain scalable ML pipelines using SAS Viya and Python-based ML frameworks
- Lead feature engineering and data preprocessing for structured financial and taxation datasets
- Train tune and validate ML models with a strong focus on accuracy stability and explainability
- Implement model monitoring performance tracking and drift detection mechanisms
- Collaborate with architects data engineers and domain experts to integrate ML models into enterprise systems
- Ensure ML solutions meet regulatory compliance and audit requirements
- Conduct code reviews and enforce ML engineering and software development best practices
- Mentor junior ML engineers and contribute to technical decision-making
- Support continuous improvement of ML models and platforms
Requirements
- Bachelors or Masters degree in Computer Science Data Science Engineering Statistics or a related field
- 5 years of hands-on experience in machine learning or applied data science
- Experience working with financial taxation risk or compliance-related data
- Strong hands-on experience with SAS Viya
- Strong proficiency in Python with ML libraries such as Scikit-learn TensorFlow or PyTorch
- Solid understanding of supervised and unsupervised learning feature engineering and model evaluation
- Experience with SQL and relational databases in financial environments
- Hands-on experience with ML model deployment monitoring and MLOps workflows
- Exposure to cloud platforms such as AWS Azure or GCP
- Familiarity with Spark or distributed data processing is a plus
Design develop and deploy production-grade machine learning models for financial and taxation use casesBuild and maintain scalable ML pipelines using SAS Viya and Python-based ML frameworksLead feature engineering and data preprocessing for structured financial and taxation datasetsTrain tune and va...
- Design develop and deploy production-grade machine learning models for financial and taxation use cases
- Build and maintain scalable ML pipelines using SAS Viya and Python-based ML frameworks
- Lead feature engineering and data preprocessing for structured financial and taxation datasets
- Train tune and validate ML models with a strong focus on accuracy stability and explainability
- Implement model monitoring performance tracking and drift detection mechanisms
- Collaborate with architects data engineers and domain experts to integrate ML models into enterprise systems
- Ensure ML solutions meet regulatory compliance and audit requirements
- Conduct code reviews and enforce ML engineering and software development best practices
- Mentor junior ML engineers and contribute to technical decision-making
- Support continuous improvement of ML models and platforms
Requirements
- Bachelors or Masters degree in Computer Science Data Science Engineering Statistics or a related field
- 5 years of hands-on experience in machine learning or applied data science
- Experience working with financial taxation risk or compliance-related data
- Strong hands-on experience with SAS Viya
- Strong proficiency in Python with ML libraries such as Scikit-learn TensorFlow or PyTorch
- Solid understanding of supervised and unsupervised learning feature engineering and model evaluation
- Experience with SQL and relational databases in financial environments
- Hands-on experience with ML model deployment monitoring and MLOps workflows
- Exposure to cloud platforms such as AWS Azure or GCP
- Familiarity with Spark or distributed data processing is a plus
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