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Today

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 building fraud detection and financial crime analytics across its FraudSeer, CrimeSeer, IdentitySeer, 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.

What you’ll do

Analyze large volumes of financial transaction and customer behavior data using HPCC Systems and ECL.
Develop ECL-based analytical workflows and feature-generation pipelines.
Create behavioral baselines for customers, accounts, counterparties, devices, and transaction channels.
Develop fraud and anomaly-detection methodologies for identifying unusual patterns.
Analyze first-party, second-party, and third-party fraud.
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.
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.
Design experiments and backtesting approaches using historical transaction data.
Collaborate with FraudSeer, CrimeSeer, IdentitySeer, 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.

Skills and experience

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.
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.

Technologies

HPCC Systems, ECL, SQL, Python, R

Additional focus areas

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 DataSeers HPCC/ECL environment.

What success looks like

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.

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Today

ETL Developer / HPCC Data Engineer

DATASEERS INC Alpharetta, GA

Alpharetta, GA

Today

Full time Backend Developer Big Data Data Analysis

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Job Description

DataSeers Inc builds large-scale financial data pipelines to power downstream products. This onsite role in Alpharetta, GA focuses on creating and improving ETL processes using HPCC Systems and ECL, helping ensure financial datasets are ingested, normalized, transformed, linked, enriched, and validated for accuracy and downstream reliability.

What you will do

Develop, maintain, and optimize ETL workflows using HPCC Systems and ECL.
Build ECL datasets, records, transforms, joins, rollups, deduplication, and data validation logic.
Ingest structured and semi-structured financial data from multiple sources.
Create data mappings from client source formats into DataSeers canonical data structures.
Normalize inconsistent data across customers, accounts, business, signers, beneficial owners, transactions, and counterparties.
Implement entity-linking and relationship logic, including complex joins and matching across financial datasets.
Troubleshoot issues such as incorrect joins, duplicate records, missing relationships, data-shifting, malformed files, and inconsistent identifiers.
Build reusable ECL components and data-processing patterns.
Optimize HPCC jobs for performance and scalability, including analysis of Thor and Roxie workloads where applicable.
Develop validation and reconciliation routines to support data completeness and accuracy.
Work with MySQL, Elasticsearch, Kafka, APIs, and other parts of the DataSeers ecosystem.
Support batch and near-real-time processing requirements.
Collaborate with customer implementation teams to understand source data and integration requirements.
Investigate data-quality issues before they impact compliance, fraud, reconciliation, or reporting systems.
Participate in code review, testing, deployment validation, and production troubleshooting.

What you bring

3+ years of ETL, data engineering, or large-scale data-processing experience.
Hands-on experience with HPCC Systems.
Strong ECL development experience.
Strong grasp of ECL concepts: datasets, records, transforms, joins, normalize, denormalize, rollups, deduplication, and distributed processing.
Strong SQL skills.
Solid understanding of data structures, relationships, and data modeling.
Experience troubleshooting complex joins and data relationships.
Experience working with large datasets.
Understanding of data quality, reconciliation, referential integrity, and exception handling.
Experience with Git or equivalent source control.
Strong analytical and debugging skills.

Technologies

HPCC Systems, ECL, SQL
MySQL, Elasticsearch, Kafka, APIs
Thor, Roxie, Git

Preferred experience

Banking, fintech, payments, or financial services experience.
Experience with ACH, wires, cards, RTP, FedNow, checks, or other payment data.
HPCC Thor and Roxie experience.
Experience optimizing large ECL jobs.
Elasticsearch experience.
MySQL experience.
Kafka or event-driven processing experience.
Experience with AML, fraud, KYC/KYB, or transaction monitoring.
Python, JavaScript, Node.js, or Java experience is useful, but secondary to HPCC/ECL expertise.

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1mo ago

Data Scientist

SAIC McLean, VA

McLean, VA

1mo ago Closed

$240k - $280k/yr Full time Advanced Analytics Data Data Analysis

This position is no longer accepting applications (closed Sep 6, 2026).

Job Description

Join SAIC as a Lead Data Scientist on the Data Modeling Team in McLean, VA (onsite). This role supports technical targeting analytics, mission-relevant reporting, and large-scale parallel processing, combining expertise across ECL, HPCC, and modern programming languages. You will work with high-volume structured and unstructured data while contributing to analytics that need rigor, validation, and modeling discipline.

The position is based in McLean, VA and includes a TS/SCI with polygraph requirement. Compensation for this role is USD 240,001 - 280,000 per year, based on experience and qualifications.

Responsibilities

Provide technical targeting analytic support for the Sponsor across a spectrum of mission spaces, applying analytic judgement to data presented.
Manipulate high-volume structured and unstructured data to conduct analysis and produce mission relevant analytic reporting/products.

Requirements

Bachelor’s degree plus 14 years or more experience; or Master’s plus 12 years or more experience; or PhD or JD plus 9 years or more experience (experience equivalence accepted).
Demonstrated experience with ECL and HPCC (minimum 5 years in ECL).
Demonstrated experience using analytic techniques and tools to deliver technical targeting analytic support across mission spaces, including applying analytic judgement to presented data.
Demonstrated experience manipulating high-volume structured and unstructured data to perform analysis and generate mission-relevant analytic reporting/products.
Demonstrated experience with computer languages (for example Python, C, SQL) to perform large scale parallel processing of the Sponsor’s data, complementing ECL.
Demonstrated experience performing large scale parallel processing and developing, validating, and using methodologies to support analytic requirements in Clustered Computing environments.
Demonstrated experience with data modeling.
TS/SCI with polygraph.

Technology Focus

ECL
HPCC
Python
C
SQL

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