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DataVisorDataVisorMountain View, CA

Data Scientist, AI Solutions

Hands-on Data Scientist building pre-built fraud detection models and AI strategies for real-time payments and AML solutions. Requires MS degree, 1+ year experience, Python/SQL proficiency, and statistical modeling skills.

120k – 170k
On-site1+ YOEData Science

About the role

Responsibilities

  • Develop Pre-Built Detection Models: Design, back-test, and optimize statistical baselines and machine learning strategies for core solution modules including Real-Time Payments (RTP), ACH, Wire, Check, and Application/Onboarding.
  • Mine the Global Consortium: Analyze large-scale, cross-industry data within the global intelligence network to identify high-risk device fingerprints and patterns of organized fraud, transforming insights into deployable features.
  • Architect "Cold Start" Logic: Create generalized scoring models that deliver immediate value to new clients, ensuring protection against known threats before historical data integration.
  • Validate AI Agent Logic: Serve as the expert "Human-in-the-Loop" for the AI-driven strategy engine, rigorously testing and validating automated fraud detection logic for safety, transparency, and low false positive rates.
  • Cross-Functional R&D: Collaborate with Product, Strategy, Data Science, Delivery, and Engineering teams to explore and implement state-of-the-art machine learning and large language model (LLM) capabilities, providing statistical rigor to turn experimental concepts into production-grade features.

Requirements

  • Education: MS in Computer Science, Statistics, Mathematics, Engineering, or a related discipline.
  • Experience: Minimum 1 year of hands-on experience in Data Science or Advanced Analytics.
  • Technical Core: Proficiency in Python (Pandas, NumPy, Scikit-learn) and SQL.
  • Statistical Rigor: Solid foundation in statistical modeling, feature selection, and performance evaluation (Precision/Recall, AUC, KS).

Preferred Qualifications

  • Experience with graph theory or link analysis for detecting network-based fraud.
  • Familiarity with unsupervised learning techniques or anomaly detection.
  • Previous experience working in a high-growth SaaS or Fintech environment.
  • Domain Knowledge: Familiarity with Fraud Detection, Credit Risk, or Trust & Safety, including knowledge of payment rails (FedNow, ACH, Wire) and typologies (Synthetic ID, ATO, Kiting).

Compensation & Benefits

  • Salary ranges between USD 120,000 and 170,000.
  • Total compensation includes base salary, performance bonuses, and equity options.
  • Comprehensive medical, dental, and vision insurance coverage.
  • 401(k) retirement savings plan available.
  • Flexible Time Off (FTO) plus paid holidays.
  • Opportunities for research, development, and professional advancement.
  • Regular team-building events in a collaborative and innovative work environment.

Skills

PythonpandasNumPyscikit-learnSQLStatistical ModelingFeature SelectionGraph TheoryUnsupervised LearningAnomaly Detection

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