Plaid
Senior Data Scientist - Fraud
Similar Jobs
Cloud • Information Technology • Machine Learning
Lead technical programs delivering GPU and CPU compute fleets from infrastructure handover through provisioning, validation, operational acceptance, and steady-state operations. Translate customer and business requirements into delivery plans, manage dependencies and blockers, assess capacity gaps, drive prioritization, establish readiness criteria, and coordinate cross-functional engineering and operations teams. Define delivery metrics, improve forecasting, and identify tooling, automation, and process improvements to increase provisioning throughput and operational readiness.
Top Skills:
Computer NetworkingCpuData Center InfrastructureGpuHardware/Software IntegrationInfrastructure AutomationServer ProvisioningSQL
Consumer Web • Coupons • Healthtech • Social Impact • Pharmaceutical
Lead a 10+ person marketing science team to centralize customer segmentation, programmatic media and audience strategy, marketing analytics and BI, attribution (lift and econometrics), ad operations, privacy/compliance, and martech. Build data products and automation for scalable media activation, provide reporting and insights for stakeholders and ELT, and collaborate cross-functionally to align measurement and marketing strategy.
Top Skills:
Ad OperationsAi ToolsBusiness IntelligenceCustomer SegmentationEconometric ModelingLift MeasurementMarketing AutomationMartechProgrammatic Media
Artificial Intelligence • Cloud • Computer Vision • Hardware • Internet of Things • Software
Manage enterprise customer implementations of Samsara’s IoT platform, from deployment and launch planning through adoption and account health tracking. Build trusted relationships with executives and users, provide tailored training, explain technical capabilities, and coordinate with Sales, Support, Sales Engineering, and Product. The role requires strong consultative communication, organization, technical-product understanding, and the ability to manage multiple customer engagements in a remote environment.
Top Skills:
Internet Of Things (Iot)SaaSSamsara Connected Operations CloudVehicle Telematics
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, Washington D.C., London and Amsterdam.
The Data team within Plaid’s Fraud organization builds the machine learning systems that power Plaid’s newest fraud detection products, leveraging the breadth of Plaid’s network data to stop fraud before it occurs. We manage the full lifecycle of these systems—from feature pipeline development and model training to deployment, serving, and monitoring—ensuring our solutions scale reliably as we grow to support hundreds of customers.
As a Data Scientist on Plaid’s Fraud Data team, you will analyze customer and network traffic to understand how Protect performs across different segments and use cases. You’ll build dashboards and performance metrics that create a clear, shared view of product health for both the team and our go-to-market partners. You will run backtests on customer traffic to evaluate model and rule performance, uncover high-value opportunities, and generate insights that support sales motions and customer expansion. You’ll also design the underlying data models and schemas that enable efficient, reliable analysis and reporting. In close partnership with Product and Engineering, you will help design and evaluate experiments that shape new customer-facing features and inform the future of our fraud products.
Responsibilities
- Work at the intersection of product analytics, machine learning, and fraud/risk to drive meaningful product improvements.
- Own the metrics, dashboards, and experimentation frameworks that inform product strategy and decision-making.
- Analyze complex datasets to uncover clear, actionable insights that shape product direction.
- Partner with go-to-market teams to demonstrate the technical and business value of our products to customers.
Qualifications
- 5+ years of total experience, including at least 2–3 years working deeply with product analytics, machine learning. experimentation, or data-driven products.
- Strong proficiency in SQL and Python.
- Hands-on experience with product analytics, experimentation frameworks, or backtesting methodologies.
- Skilled in designing, building, and maintaining dashboards and core product performance metrics.
- Capable of designing and interpreting backtests or offline evaluations for ML and rules-based systems.
- Excellent communicator with strong stakeholder-management skills across diverse teams.
- Background in fraud or risk domains — Nice to have.
- Familiarity with data-insights products and a solid understanding of model-performance metrics — Nice to have.
- Exposure to customer-facing or GTM-facing analytics — Nice to have.
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 [email protected].
Please review our Candidate Privacy Notice here.
Plaid San Francisco, California, USA Office
San Francisco, CA, United States, 94105
What you need to know about the San Francisco Tech Scene
San Francisco and the surrounding Bay Area attracts more startup funding than any other region in the world. Home to Stanford University and UC Berkeley, leading VC firms and several of the world’s most valuable companies, the Bay Area is the place to go for anyone looking to make it big in the tech industry. That said, San Francisco has a lot to offer beyond technology thanks to a thriving art and music scene, excellent food and a short drive to several of the country’s most beautiful recreational areas.
Key Facts About San Francisco Tech
- Number of Tech Workers: 365,500; 13.9% of overall workforce (2024 CompTIA survey)
- Major Tech Employers: Google, Apple, Salesforce, Meta
- Key Industries: Artificial intelligence, cloud computing, fintech, consumer technology, software
- Funding Landscape: $50.5 billion in venture capital funding in 2024 (Pitchbook)
- Notable Investors: Sequoia Capital, Andreessen Horowitz, Bessemer Venture Partners, Greylock Partners, Khosla Ventures, Kleiner Perkins
- Research Centers and Universities: Stanford University; University of California, Berkeley; University of San Francisco; Santa Clara University; Ames Research Center; Center for AI Safety; California Institute for Regenerative Medicine
