Data Scientist
Role details
Job location
Tech stack
Job description
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.
What you will do:
- 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., * This role is on-site Monday through Friday. Fiserv considers in-person collaboration to be an essential part of this role as in-person office experiences help you with your overall onboarding experience and leads to stronger productivity.
- This role requires the use of a computer and audio equipment.
Sponsorship:
- You must currently possess valid and unrestricted U.S. work authorization to be considered for this role. Individuals with temporary visas including, but not limited to, F-1 (OPT, CPT, STEM), H-1B, H-2, or TN, or any candidate requiring sponsorship, now or in the future, will not be considered for this role.
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Salary Range $111,000.00 - $188,400.00
These pay ranges apply to employees in New Jersey and New York. Pay ranges for employees in other states may differ.
It is unlawful to discriminate against a prospective employee due to the individual's status as a veteran.
For incentive eligible associates, the successful candidate is eligible for an annual incentive opportunity which may be delivered as a mix of cash bonus and equity awards in the Company's sole discretion.
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.
What would be great to have:
- 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
