Senior Data Scientist - Fraud/Risk

Fiserv • Berkeley Heights, NJ

3d • Onsite

About The Position

Calling all innovators - find your future at Fiserv. We're Fiserv, a global leader in Fintech and payments, and we move money and information in a way that moves the world. We connect financial institutions, corporations, merchants and consumers to one another millions of times a day - quickly, reliably, and securely. Any time you swipe your credit card, pay through a mobile app, or withdraw money from the bank, we're involved. If you want to make an impact on a global scale, come make a difference at Fiserv. What does a Senior Data Scientist do at Fiserv Data Commerce Solutions? As a Senior Data Scientist for Fraud and Risk within Data Commerce Solutions (DCS), you will operate at the epicenter of the payment ecosystem, architecting AI-driven products that transform Fiserv’s massive transaction streams into high-fidelity behavioral risk signals across every layer of the digital economy. By leveraging advanced Machine Learning, Deep Learning, and rigorous statistical analysis, you will build commercial-grade solutions that empower financial institutions and merchants to detect sophisticated patterns at the consumer identity, transaction, card/account, and business levels. Your work will drive innovation across the payment lifecycle to combat complex threats including Friendly Fraud, Account Takeover (ATO), Synthetic Identities, Money Mule activity, and AML/financial crime while ensuring all models adhere to the highest standards of financial governance and regulatory compliance.

Requirements

• 7+ years of experience leveraging large scale datasets to develop tactical insights into fraud typologies like ATO, Synthetic ID, and AML using ML, RAG, and NLP.
• 7+ years of experience formulating research problems, designing champion/challenger back-tests, and implementing production-ready solutions in a financial or high-growth tech environment.
• Experience with anomaly detection, credit risk modeling, and adversarial machine learning within merchant and banking ecosystems.
• Mastery of high-precision classification, anomaly detection, and clustering techniques, with a focus on non-stationary time series analysis, Bayesian inference, causal analysis, and survival analysis to model risk probabilities, event timing, and evolving fraud trends.
• Expert proficiency in Python, SQL, and PySpark for high-volume transaction processing, with hands on use of Scikit-learn, XGBoost, and LightGBM and Deep Learning and Agentic AI frameworks for threat hunting, and graph databases like Neo4j or Tiger graph for fraud network analysis.
• Experience with Databricks and Snowflake, SageMaker or Azure ML, feature stores (e.g., Tecton, Feast) and streaming architectures (Kafka, Flink).
• Proficiency in tokenization and embeddings, with hands-on experience tuning and deploying Large Language Model architectures such as LLaMA, BERT, or Transformers.
• Bachelor’s degree in a quantitative field such as Computer Science, Mathematics, Artificial Intelligence, Financial Engineering, or Statistics.

Nice To Haves

• Master’s degree or PhD
• Prior experience working within fraud prevention units, risk management at Tier-1 banks, or fintech firms, with a proven ability to deliver production-grade models.
• Published research in top-tier quantitative or AI journals or a top-tier Kaggle ranking in anomaly detection or financial forecasting.
• Significant contributions to GitHub projects involving financial modeling, LLM orchestration, or Explainable AI (XAI) frameworks.

Responsibilities

• Monetize Risk Intelligence: Architect and deploy production-grade AI/ML frameworks to monetize Fiserv’s unique data footprint into inventive risk scores and insights that detect identity, transaction, and business-level threats.
• Architect Financial AI: Build custom GenAI, NLP, and LLM models for high-velocity stream processing, focusing on extracting risk indicators and behavioral anomalies from structured transaction data and unstructured metadata.
• Next-Gen Frameworks: Implement LangChain and LlamaIndex to develop RAG and Agentic AI frameworks that enable institutional clients to query and interact with complex, multi-dimensional risk datasets.
• Quantitative Collaboration: Work in a high-performance team environment, collaborating with Product Managers, payment system experts, and Engineering to deploy and monitor production-grade AI and ML models.
• Strategic Synthesis: Distill complex quantitative risk insights into high-level investment and risk theses for executive leadership and sophisticated external stakeholders.
• Data Stewardship & Compliance: Partner with the Data Usage Committee, Model Governance, Legal, and Compliance teams to ensure data privacy and adherence to strict data usage rights within the DCS framework.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level