About the role
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
Originally posted on Himalayas