Data Scientist, Financial Crime & Fraud Analytics

DataSeers Inc is seeking a Data Scientist to support fraud detection and financial crime analytics using HPCC Systems and ECL in Alpharetta, GA.

Responsibilities

• Analyze large volumes of financial transaction and customer behavior data using HPCC Systems and ECL.
• Build ECL-based analytical workflows and feature-generation pipelines.
• Develop behavioral baselines for customers, accounts, counterparties, devices, and transaction channels.
• Design fraud and anomaly-detection methodologies across multiple product areas.
• Conduct analysis of first-party, second-party, and third-party fraud patterns.
• Detect unusual transaction behavior including velocity, amount, frequency, geography, channel, and relationship changes.
• Perform relationship-based analysis across senders/receivers, accounts, businesses, devices, IP addresses, geographies, and payment instruments.
• Create features for account takeover, mule activity, transaction anomalies, duplicate payments, relationship changes, and behavioral shifts.
• Apply network and relationship analysis to identify suspicious linkages.
• Evaluate and refine existing fraud and AML rules using historical data.
• Find opportunities to reduce false positives while maintaining detection effectiveness.
• Develop statistical and machine-learning models where appropriate.
• Work with labeled and unlabeled datasets.
• Design experiments and backtesting approaches using historical transaction data.
• Partner with FraudSeer, CrimeSeer, IdentitySeer, ETL, and platform engineering teams.
• Translate analytical research into production detection logic.
• Develop approaches that are interpretable and explainable for financial institutions.
• Research emerging financial crime and fraud typologies.

Requirements

• Degree in computer science, statistics, mathematics, data science, engineering, economics, or another quantitative discipline.
• 3+ years of professional experience in data science, quantitative analytics, or fraud analytics.
• Hands-on experience with HPCC Systems and/or ECL, or strong willingness and demonstrated ability to become productive in ECL quickly.
• Strong SQL skills.
• Strong statistical and analytical skills.
• Experience working with very large datasets.
• Understanding of classification, clustering, anomaly detection, feature engineering, statistical testing, and model evaluation.
• Ability to analyze complex relationships between entities and transactions.
• Ability to clearly explain analytical results to product, engineering, compliance, and business teams.

Preferred Experience

• Strong HPCC/ECL experience.
• Fraud detection or financial crime analytics experience.
• Banking, fintech, cards, or payments experience.
• Graph or network analytics.
• Time-series and behavioral analytics.
• Experience with account takeover, mule detection, synthetic identity, payment fraud, or transaction monitoring.
• Python experience for research, experimentation, or model development.
• Experience taking analytical models from research into production.
• Familiarity with AML transaction monitoring and regulatory expectations.

Technologies

• HPCC Systems
• ECL
• SQL
• Python
• R

Note: This is not primarily a Python notebook position. Python, R, and traditional machine-learning frameworks may be used where appropriate, but the core work involves substantial analytical execution against large-scale financial datasets in the DataSeers HPCC/ECL environment.

What Success Looks Like

• Start with billions of financial transactions.
• Use HPCC/ECL to transform transactions into behavioral and relationship features.
• Identify fraud or financial crime patterns and validate them using historical analysis.
• Work with engineering to turn patterns into scalable production detection capabilities.