Senior Data Scientist - Fraud Prevention
Role details
Job location
Tech stack
Job description
The Fraud Prevention organization at Nextdoor is dedicated to protecting neighbors from harmful content, fraud, and abuse, and ensuring that neighbors can safely build and participate in local communities on our platform. The team - which consists of Product, Engineering, Design, and Neighborhood Operations (NOPS) - helps develop policies, build detection systems, and deliver moderation tools at scale to keep our platform safe., * Analyze large, complex datasets to identify abuse patterns, fraud signals, and harmful behavior trends.
- Conduct root cause analysis to diagnose safety incidents and emerging risks.
- Evaluate new tool effectiveness (including AI), and impact on agent efficiency and user satisfaction.
- Define and track core metrics (e.g., harm prevalence, violation rates, detection accuracy).
- Navigate the tradeoff between operational efficiency, safety, and user growth/experience.
- Build dashboards and reporting frameworks to track platform health and safety performance.
_Develop Models & Rules
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- Develop heuristics, statistical models, and machine learning solutions for proactive detection of abuse, fraud, or harmful content
- Build prediction systems (e.g., anomaly detection, risk scoring, behavioral profiling).
- Improve automated enforcement and moderation workflows.
- Evaluate model performance and iterate on detection strategies.
_Evaluate Product / Policy Changes Via Experimentation
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- Design and analyze experiments (A/B tests, causal inference) to measure safety feature impact (e.g., login & verification, AI moderation support). Clearly communicate findings to technical and non-technical stakeholders.
- Quantify tradeoffs between operational efficiency, safety, and user growth/experience. Guide TnS team on key tradeoffs in decision-making
_Own Cross-Functional Partnership
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- Partner with Product, Engineering, Operations, Policy, and Legal teams to define safety strategy.
- Influence decision-making through data storytelling and insights.
- Standardize analytical methodologies and tools for scalable decision-making.
Requirements
- Bachelor's or Master's degree in Statistics, Computer Science, Mathematics, Economics, or a related quantitative field.
- 5+ years of Data Science experience working with large-scale data and statistical analysis, including 1+ year of data science experience in fraud prevention, moderation, or risk.
- Strong analytical and problem-solving skills, with a track record to lead projects from concept to impact.
- Proficiency in SQL and at least one scripting language (e.g., Python or R).
- Expertise in experimentation and causal inference (A/B testing, cohort analysis, pre/post analysis) to evaluate product or policy changes in production environments.
- Hands-on experience with standard Machine Learning and statistical methods (e.g., prediction, classification, anomaly detection, time series), ideally in risk or fraud prevention contexts.
- Ability to collaborate cross-functionally with Product, Engineering, Operations, and Legal/Policy partners; comfortable influencing without direct authority.
- Strong communication, with the ability to translate technical concepts to non-technical stakeholders, including operations leaders and executives.
Nice to Have
- 2+ years of experience in fraud prevention, moderation, or risk in a social platform context.
- Strong experience with anomaly detection and building/deploying machine learning models.
- Experience working with text, image, or behavioral data.