Senior Data Scientist - Fraud Prevention

Requirements

• 2+ years of experience in fraud prevention, moderatio

Benefits

Additional Information

#Team Nextdoor Nextdoor (NYSE: NXDR) is the essential neighborhood network. Neighbors, public agencies, and businesses use Nextdoor to connect around local information that matters in more than 350,000 neighborhoods across 11 countries. Nextdoor builds innovative technology to foster local community, share important news, and create neighborhood connections at scale. Download the app and join the neighborhood at nextdoor.com . Meet Your Future Neighbors 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. We are seeking a Senior Data Scientist, Fraud Prevention, to help protect our users, platform integrity, and community experience by developing strong data foundations, robust metrics and measurement frameworks, and advanced analytics and modeling that improve harm prevention and detection and moderation effectiveness. In essence, this role is integral to high-impact, cross-functional initiatives that help inform and shape our strategy and execution. At Nextdoor, we operate in an AI-first environment and expect every team member to actively use AI tools as part of their workflow. We aren't looking for prompt engineers; we're looking for people who use tools like Claude, Gemini, ChatGPT, and Glean to challenge their own thinking and take full ownership of AI-assisted outputs. We also offer a warm and inclusive work environment that embraces a hybrid employment model, blending an in office presence and work from home experience for our valued employees. The hiring team will go over these expectations with you if you are being considered for a role near one of our offices in San Francisco, Los Angeles, Chicago, Dallas, New York, and London. The Impact You'll Make Design and Lead Key Analyses and Metric Evolution 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 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 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 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. What You'll Bring To The Team 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.

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