Machine Learning Architect (SAS Viya) (8 months contract)

• 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