The ad says“Strong technical depth in Python, SQL, statistics, product analytics, and applied modeling”
The ad says“Experience applying causal inference techniques to complex product or risk problems”
The ad says“Experience with dbt or similar data transformation and analytics engineering tools”
The ad says“Apply fraud expertise, product analytics, and customer-facing data science to drive end-to-end product and business impact”
The ad says“Experience working with graph-based data or systems to identify fraud patterns and improve model performance”
The ad says“Strong experience in product analytics, metric design, and measuring product performance”
The ad says“Experience using model interpretability techniques across both traditional machine learning and modern model architectures”
The ad says“Strong experience in product analytics, metric design, and measuring product performance”
The ad says“Strong technical depth in Python, SQL, statistics, product analytics, and applied modeling”
The ad says“Strong technical depth in Python, SQL, statistics, product analytics, and applied modeling”
The ad says“Strong technical depth in Python, SQL, statistics, product analytics, and applied modeling”
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
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
Benefits
Vibrant offices in SF, NYC, and Raleigh-Durham—with catered meals, happy hours, and clubs to keep you connected
Competitive pay, comprehensive health benefits, and support for fertility, mental health, and parental leave
Lifestyle perks including home office stipends, daycare support, and commuting benefits like CitiBike and Lyft
Preferred experience
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
Strong experience in product analytics, metric design, and measuring product performance
Strong technical depth in Python, SQL, statistics, product analytics, and applied modeling
Experience partnering directly with customers to deliver data-driven insights and solutions
Proven experience managing, mentoring, and developing high-performing data scientists
Deep domain expertise in fraud, risk, or related areas
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!
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
