Data Scientist, Financial Crime & Fraud Analytics

Data Seers Inc is building fraud detection and financial crime analytics across its Fraud Seer, Crime Seer, Identity Seer, and related products. In this onsite role in Alpharetta, GA, you will use HPCC Systems and ECL to turn large-scale transaction and customer behavior data into behavioral baselines, anomaly signals, and production-ready detection logic.

• Analyze large volumes of financial transaction and customer behavior data using HPCC Systems and ECL
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• Develop ECL-based analytical workflows and feature-generation pipelines
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• Create behavioral baselines for customers, accounts, counter parties, devices, and transaction channels.
• Develop fraud and anomaly-detection methodologies for identifying unusual patterns.
• Analyze first-party, second-party, and third-party fraud
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• Identify unusual transaction velocity, amount, frequency, geography, channel, and relationship patterns.
• Analyze relationships between senders, receivers, accounts, businesses, devices, IP addresses, geographies, and payment instruments.
• Build features for account takeover detection, mule activity, transaction anomalies, duplicate payments, relationship changes, and behavioral shifts.
• Perform network and relationship-based analysis
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• Evaluate existing fraud and AML rules against historical data.
• Find opportunities to reduce false positives without reducing detection effectiveness.
• Develop statistical and machine-learning models where appropriate, working with labeled and unlabeled datasets
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• Design experiments and backtesting approaches using historical transaction data.
• Collaborate with Fraud Seer, Crime Seer, Identity Seer, ETL, and platform engineering teams to move from research to production.
• Translate analytical research into production detection logic suitable for financial institutions.
• Develop interpretable and explainable approaches.
• Research emerging financial crime and fraud typologies.

• Degree in computer science, statistics, mathematics, data science, engineering, economics, or another quantitative discipline.
• 3+ years of professional data science, quantitative analytics, or fraud analytics experience.
• Hands-on experience with HPCC Systems and/or ECL
  , or a strong willingness and demonstrated ability to become productive in ECL quickly.
• Strong SQL skills.
• Strong statistical and analytical skills
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• Experience working with very large datasets
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• 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.

• HPCC Systems, ECL, SQL, Python, R

• Not primarily a Python notebook position
  . Python and R may be used where appropriate, but the role requires substantial analytical work against large-scale financial datasets using the Data Seers HPCC/ECL environment
  .

You can start with billions of financial transactions, transform them with HPCC/ECL into behavioral and relationship features, detect patterns linked to fraud or financial crime, validate those patterns using historical data, and partner with engineering to deliver scalable production detection capabilities.