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)
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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
- 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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