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.
About the job
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
Python, pandas, NumPy, scikit-learn, SQL, Statistical Modeling, Feature Selection, Graph Theory, Unsupervised Learning, Anomaly Detection
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