高级机器学习数据科学家
Senior Machine Learning Data Scientist
我们正在寻找一位资深机器学习数据科学家，加入欺诈与机器学习团队，该团队是Extend后购买保护平台的核心。作为资深机器学习数据科学家，你将负责基于数亿用户的信号和交易开发先进的机器学习模型，以检测和防止欺诈、评估风险并释放商业价值。
你将主导整个数据科学生命周期——从需求分析、特征工程到模型开发、评估和监控。你将与产品、工程和我们的欺诈情报团队紧密合作，将杂乱的数据转化为可扩展的、生产级的ML系统，有效阻止恶意行为。如果你是目标驱动型人才，热衷于在核心机器学习与欺诈预防的交叉领域解决复杂问题，你将在我们团队中茁壮成长！
职责包括：
- 主导模型生命周期：需求分析、实验、模型开发、评估和模型卡片，与机器学习工程师合作进行部署和生产基础设施建设
- 将复杂的欺诈模式转化为明确的ML解决方案：定义需要建模的内容、成功标准以及ML在哪些方面比简单方法更有价值
- 设计和维护用于模型开发的特征工程流水线
- 监控生产环境中的模型质量，跟踪性能随时间的变化，检测数据漂移，并确定何时需要重新训练
- 与领导层、市场推广、欺诈运营、产品和工程团队紧密合作
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About Extend:
Extend is revolutionizing the post-purchase experience for retailers and their customers by providing merchants with AI-driven solutions that enhance customer satisfaction and drive revenue growth. Our comprehensive platform offers automated customer service handling, seamless returns/exchange management, end-to-end automated fulfillment, and product protection and shipping protection alongside Extend's best-in-class fraud detection. By integrating leading-edge technology with exceptional customer service, Extend empowers businesses to build trust and loyalty among consumers while reducing costs and increasing profits.
Today, Extend works with more than 1,000 leading merchant partners across industries, including fashion/apparel, cosmetics, furniture, jewelry, consumer electronics, auto parts, sports and fitness, and much more. Extend is backed by some of the most prominent technology investors in the industry, and our headquarters is in downtown San Francisco.
About the Role:
The Fraud & Machine Learning team is the secret sauce behind Extend’s post-purchase protection platform. As a Senior ML Data Scientist, you will own the development of cutting-edge machine learning models based on signals and transactions from hundreds of millions of users to detect and prevent fraud, assess risk, and unlock business value.
You will drive the full data science lifecycle - from requirements and feature engineering through model development, evaluation, and monitoring. You’ll partner closely with Product, Engineering, and our Fraud Intelligence team to translate messy data into scalable, production-grade ML systems that stop bad actors in their tracks. If you’re impact-driven and excited to tackle complex problems at the intersection of core machine learning and fraud prevention, you’ll thrive on our team!
What You’ll Be Doing:
- Own the model lifecycle: requirements, experimentation, model development, evaluation, and model cards, partnering with ML engineers on deployment and production infrastructure
- Translate complex fraud patterns into well-framed ML solutions: defining what to model, what success looks like, and where ML adds value vs. simpler approaches
- Design and maintain feature engineering pipelines for model development
- Monitor model quality in production, tracking performance over time, detecting data drift, and determining when to retrain
- Partner closely with leadership, go-to-market, fraud operations, product, and engineering teams to define and execute effective fraud strategies
- Champion a culture of continuous learning, experimentation, and collaboration across the fraud and broader data science teams
What We’re Looking For:
Required:
- Hands-on, proactive, and analytical professionals who are passionate about using data to solve complex, real-world problems
- Bachelor’s degree or higher in a quantitative field such as Mathematics, Statistics, Computer Science, Engineering, Operations Research, Physics or related field
- 3+ years of work experience building and deploying machine learning systems into production
- Strong proficiency in Python and SQL
- Strong understanding of ML fundamentals: model selection, evaluation methodology, feature engineering, and common failure modes
- Hands-on experience with PyTorch, scikit-learn, and XGBoost (or similar gradient boosting frameworks)
- High attention to detail, strong intellectual curiosity, and a deep understanding of user behavior and fraud patterns
- Empathetic, humble, and collaborative team player
- Candidates must be located within the continental United States
Preferred:
- Experience building fraud detection or risk assessment systems
- Experience with cloud ML platforms, particularly AWS (e.g., SageMaker)
- Experience with graph data and graph-based models (e.g., PyTorch Geometric)
- Experience with model monitoring and observability tooling (e.g., Arize)
Estimated Pay Range: $135,000 - $165,000 per year salaried*
* The target base salary range for this position is listed above. Individual salaries are determined based on a number of factors including, but not limited to, job-related knowledge, skills and experience.
Life at Extend:
- Working with a great team from diverse backgrounds in a collaborative and supportive environment.
- Competitive salary based on experience, with full medical and dental & vision benefits.
- Stock in an early-stage startup growing quickly.
- Generous, flexible paid time off policy.
- 401(k) with Financial Guidance from Morgan Stanley.
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