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Data Scientist, Financial Crime & Fraud Analytics

DATASEERS INC Alpharetta, GA

Alpharetta, GA

Today

Full time Big Data Data Analysis Data Analytics

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Job Description

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.

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