出海雷达

高级机器学习工程师 (SAS Viya) (8个月合同)

Senior Machine Learning Engineer (SAS Viya) (8 months contract)

AI开发工程未标注地域
公司George Bernard Consulting
薪资未公开
工作地点Sri Lanka
地域资格未标注地域
时区要求日间重叠约 6 小时,基本正常作息
用工类型Contractor
发布时间12 天前
数据来源Himalayas
前往 Himalayas 查看并投递 →
  • 为金融和税务应用场景设计、开发和部署生产级机器学习模型
  • 使用SAS Viya和基于Python的机器学习框架构建和维护可扩展的ML流水线
  • 领导结构化金融和税务数据集的特征工程和数据预处理
  • 以准确率、稳定性和可解释性为重点,训练、调优和验证机器学习模型
  • 实现模型监控、性能跟踪和漂移检测机制
  • 与架构师、数据工程师和领域专家协作,将机器学习模型集成到企业系统中
  • 确保机器学习解决方案符合监管、合规和审计要求
  • 进行代码审查,推行机器学习工程和软件开发的最佳实践
  • 指导初级机器学习工程师,并参与技术决策
  • 支持机器学习模型和平台的持续改进

要求

  • 计算机科学、数据科学、工程、统计学或相关领域的学士或硕士学历
  • 5年以上机器学习或应用数据科学的实际工作经验
  • 具有金融、税务、风险或合规相关数据的工作经验
  • 熟练使用SAS Viya
  • 精通Python,熟悉Scikit-learn、TensorFlow或PyTorch等机器学习库
  • 熟悉监督学习和无监督学习、特征工程及模型评估
  • 具备在金融环境中使用SQL和关系型数据库的经验
  • 具备机器学习模型部署、监控和MLOps工作流的实际经验
  • 了解AWS、Azure或GCP等云平台者优先
  • 熟悉Spark或分布式数据处理者优先

Originally posted on Himalayas

查看英文原文
  • 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

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