- 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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