Senior Data Scientist (Fraud Prevention)

TL;DR

Senior Data Scientist (Fraud Prevention): Building and optimizing data foundations, robust metrics, and advanced analytics to improve harm prevention and detection for a neighborhood network platform with an accent on analyzing complex datasets, developing machine learning models for proactive detection, and evaluating product/policy changes via experimentation. Focus on quantifying tradeoffs between operational efficiency, safety, and user growth, and influencing decision-making through data storytelling.

Location: Remote within the US. Hybrid options are available if you are being considered for a role near offices in San Francisco, Los Angeles, Chicago, Dallas, New York, and London.

Salary: $175,000–$234,000 (annualized base for San Francisco, CA area, plus meaningful equity grant).

Company

hirify.global is an essential neighborhood network connecting neighbors, public agencies, and businesses across 11 countries.

What you will do

• Analyze large, complex datasets to identify abuse patterns, fraud signals, and harmful behavior trends.
• Conduct root cause analysis for safety incidents and define core metrics (e.g., harm prevalence, detection accuracy).
• 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) and improve automated enforcement workflows.
• Design and analyze experiments (A/B tests, causal inference) to measure safety feature impact.
• Partner with Product, Engineering, Operations, Policy, and Legal teams to define safety strategy and influence decision-making.

Requirements

• Bachelor’s or Master’s degree in a quantitative field (Statistics, Computer Science, Mathematics, Economics).
• 5+ years of Data Science experience working with large-scale data and statistical analysis.
• 1+ year of data science experience in fraud prevention, moderation, or risk.
• Proficiency in SQL and at least one scripting language (Python or R).
• Expertise in experimentation and causal inference (A/B testing, cohort analysis, pre/post analysis).
• Hands-on experience with standard Machine Learning and statistical methods (prediction, classification, anomaly detection, time series).

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.

Culture & Benefits

• Warm and inclusive work environment embracing a hybrid employment experience.
• Competitive compensation package including base salary and meaningful equity grants with quarterly vesting.
• Choice between a variety of great health plans.
• Commitment to building stronger local communities and fostering workforce diversity and inclusion.
• Consideration for qualified applicants with arrest and conviction records in accordance with the San Francisco Fair Chance Ordinance.