Machine Learning Architect (SAS Viya) (8 months contract)
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Summary
An 8-month contract role defining and owning end-to-end machine learning architecture for financial and taxation platforms. The architect designs scalable, compliant ML solutions on SAS Viya, Python, and cloud platforms, establishing MLOps, model governance, and monitoring standards while mentoring ML engineers.
- Define and own the end-to-end machine learning architecture for financial and taxation platforms
- Design scalable, secure, and compliant ML solutions using SAS Viya and complementary ML technologies
- Establish architectural standards, best practices, and governance frameworks for enterprise ML systems
- Lead the design of data ingestion, feature engineering, model training, deployment, and monitoring pipelines
- Ensure ML solutions comply with regulatory, audit, data privacy, and risk management requirements
- Define and enforce MLOps standards including model lifecycle management, versioning, explainability, and performance monitoring
- Collaborate with finance, taxation, compliance, and risk stakeholders to translate business and regulatory needs into technical solutions
- Review and approve ML designs, pipelines, and deployment strategies across teams
- Evaluate and introduce new ML technologies and platforms aligned with enterprise and regulatory needs
- Provide technical leadership and mentorship to senior ML engineers and teams
Requirements
- Bachelors or Masters degree in Computer Science, Data Science, Engineering, Statistics, or a related field
- 8+ years of experience in data, analytics, or machine learning roles, with at least 4+ years in architecture or technical leadership positions
- Strong domain experience in financial services, taxation, risk, or regulatory analytics
- Extensive hands-on and architectural experience with SAS Viya
- Deep understanding of machine learning algorithms, statistical modeling, and financial data analytics
- Strong expertise in Python and ML libraries such as NumPy, Pandas, Scikit-learn, TensorFlow, or PyTorch
- Proven experience with MLOps practices, model governance, explainable AI, and risk controls
- Experience designing and deploying ML solutions on cloud platforms such as AWS, Azure, or GCP
- Strong SQL skills and experience working with large-scale financial datasets
- Knowledge of big data or distributed processing frameworks such as Spark is an advantage