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 Originally posted on Himalayas