数据科学经理 - 欺诈
Data Science Manager - Fraud
我们相信，人们与财务的互动方式将在未来几年得到显著改善。我们致力于通过构建工具和体验来推动这一变革，成千上万的开发者使用这些工具来创建他们自己的产品。Plaid 支持数以百万计的人依靠的工具，帮助他们过上更健康的财务生活。我们与 Venmo、SoFi、多家财富 500 强公司以及许多大型银行合作，让人们轻松地将财务账户连接到他们想要使用的应用和服务。Plaid 的网络覆盖美国、加拿大、英国和欧洲的 12,000 家金融机构。公司成立于 2013 年，总部位于旧金山，在纽约、西雅图、华盛顿特区、罗利、伦敦和阿姆斯特丹设有办公室。
欺诈数据是 Plaid 欺诈部门内的数据科学和机器学习团队，负责利用数据和机器学习来改进和扩展 Plaid 的欺诈产品。在欺诈数据团队中，客户与产品智能团队专注于理解产品表现、发现客户洞察，并为市场进入团队提供数据驱动的解决方案。该团队与客户和市场进入团队紧密合作，进行欺诈分析和概念验证，将客户的学习成果转化为可扩展、可重用的产品功能。我们还构建衡量产品健康状况的指标、分析和数据基础，识别改进机会，并指导 Plaid 欺诈产品组合中的产品决策。
作为数据科学经理，您将领导一个负责客户数据科学和欺诈产品分析的团队。您将制定团队的路线图，培养数据科学家，并参与分析方法、技术评审和客户调查。您将：
- 与产品、工程和市场进入团队一起制定 6-12 个月的路线图，并在团队内部分配优先级和职责。
- 定义产品指标、其底层数据以及报告和警报实践；将结果用于路线图和投资决策。
- 建立可重复的客户回顾和概念验证流程，包括数据检查、评估方法和明确的建议。
- 识别在客户分析中反复出现的欺诈信号和产品机会，并与产品和 MLE 合作开发这些机会。
- 审查分析设计、数据模型、代码和模型评估；在您的专业知识范围内直接参与调查。
查看英文原文
We believe that the way people interact with their finances will drastically improve in the next few years. We’re dedicated to empowering this transformation by building the tools and experiences that thousands of developers use to create their own products. Plaid powers the tools millions of people rely on to live a healthier financial life. We work with thousands of companies like Venmo, SoFi, several of the Fortune 500, and many of the largest banks to make it easy for people to connect their financial accounts to the apps and services they want to use. Plaid’s network covers 12,000 financial institutions across the US, Canada, UK and Europe. Founded in 2013, the company is headquartered in San Francisco with offices in New York, Seattle, Washington D.C., Raleigh, London, and Amsterdam.
Fraud Data is the data science and machine learning team within Plaid’s Fraud organization, responsible for using data and ML to improve and scale Plaid’s fraud products. Within Fraud Data, the Customer & Product Intelligence team focuses on understanding product performance, uncovering customer insights, and enabling go-to-market teams with data-driven solutions. The team partners closely with customers and GTM teams on fraud analyses and proofs of concept, turning customer learnings into scalable, reusable product capabilities. We also build the metrics, analytics, and data foundations that measure product health, identify opportunities for improvement, and guide product decisions across Plaid’s Fraud portfolio.
As a Data Science Manager, you will lead a team responsible for customer-facing data science and Fraud product analytics. You will set the team's roadmap, develop its data scientists, and remain involved in analytical methods, technical reviews, and customer investigations. You will:
- Set a 6–12-month roadmap with Product, Engineering, and GTM, and assign priorities and responsibilities across the team.
- Define product metrics, their underlying data, and reporting and alerting practices; use the results in roadmap and investment decisions.
- Establish a repeatable process for customer retrospectives and proofs of concept, including data checks, evaluation methods, and clear recommendations.
- Identify fraud signals and product opportunities that recur across customer analyses and work with Product and MLEs to develop them.
- Review analytical designs, data models, code, and model evaluations; contribute directly to investigations where your expertise is needed.
- Coach data scientists through clear expectations, regular feedback, performance discussions, and growth opportunities.
- Use AI-assisted analysis and development tools where useful, and ensure results are properly reviewed before informing customer recommendations or product decisions.
Responsibilities:
- Define how Plaid measures, evaluates, and improves the performance of its Fraud products.
- Apply fraud expertise, product analytics, and customer-facing data science to drive end-to-end product and business impact.
- Translate customer insights and fraud analyses into scalable product capabilities and opportunities for GTM growth.
- Lead and develop a high-performing team while remaining technically hands-on with critical analyses and initiatives.
- Raise the bar for product metrics, analytical rigor, and the data foundations that power decision-making across Fraud.
Qualifications:
- Proven experience managing, mentoring, and developing high-performing data scientists.
- Deep domain expertise in fraud, risk, or related areas.
- Strong experience in product analytics, metric design, and measuring product performance.
- Experience partnering directly with customers to deliver data-driven insights and solutions.
- Strong technical depth in Python, SQL, statistics, product analytics, and applied modeling.
- Demonstrated ability to set technical direction and deliver complex, high-impact initiatives through a team while remaining hands-on.
- Excellent communication and cross-functional collaboration skills across Product, Engineering, Machine Learning, GTM, and customer stakeholders.
Nice-to-Have:
- Experience working with graph-based data or systems to identify fraud patterns and improve model performance.
- Experience applying causal inference techniques to complex product or risk problems.
- Experience using model interpretability techniques across both traditional machine learning and modern model architectures.
- Experience with dbt or similar data transformation and analytics engineering tools.
Our mission at Plaid is to unlock financial freedom for everyone. To support that mission, we seek to build a diverse team of driven individuals who care deeply about making the financial ecosystem more equitable. We recognize that strong qualifications can come from both prior work experiences and lived experiences. We encourage you to apply to a role even if your experience doesn't fully match the job description. We are always looking for team members that will bring something unique to Plaid!
Plaid is proud to be an equal opportunity employer and values diversity at our company. We do not discriminate based on race, color, national origin, ethnicity, religion or religious belief, sex (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender, gender identity, gender expression, transgender status, sexual stereotypes, age, military or veteran status, disability, or other applicable legally protected characteristics. We also consider qualified applicants with criminal histories, consistent with applicable federal, state, and local laws. Plaid is committed to providing reasonable accommodations for candidates with disabilities in our recruiting process. If you need any assistance with your application or interviews due to a disability, please let us know at accommodations@plaid.com.
Please review our Candidate Privacy Notice here https://plaid.com/legal/#candidate-privacy-notice.
Additional compensation in the form(s) of equity and/or commission are dependent on the position offered. Plaid provides a comprehensive benefit plan, including medical, dental, vision, and 401(k). Pay is based on factors such as (but not limited to) scope and responsibilities of the position, candidate's work experience and skillset, and location. Pay and benefits are subject to change at any time, consistent with the terms of any applicable compensation or benefit plans.