机器学习架构师（SAS Viya）（8个月合同）

• 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