Data Scientist

Anticipated future opportunity upon contract award (Estimated start of September 2026)

Job Summary

We are seeking an experienced Data Scientist to support the Financial Crimes Enforcement Network (FinCEN) in its mission to safeguard the financial system from illicit use, combat money laundering, and counter terrorist financing. This role will focus on cleaning, analyzing, and transforming complex commercial, financial, and regulatory data to derive actionable insights in support of FinCEN’s enforcement and compliance operations. The Data Scientist will design and implement advanced analytical models, utilize machine learning methods, and develop tools to enhance data usability and analysis. As part of a multidisciplinary team, the Data Scientist will collaborate closely with investigators, analysts, and technical staff, applying their expertise to identify patterns, typologies, and anomalies related to illicit financial activity, including money laundering, terrorist financing, proliferation financing, and cybercrime.

Essential Responsibilities

Analyze and Transform Data: Extract, clean, transform, and analyze complex, large-scale datasets from various structured and unstructured sources, including BSA data, financial systems, and internal databases.

Develop Analytics and Machine Learning Models:

Build and deploy machine learning models and analytic methods to detect patterns and anomalies, such as fraud, money laundering, and other illicit finance activities.

Utilize anomaly detection methods to identify suspicious activity in financial data sets.

Apply Natural Language Processing (NLP) techniques using Python libraries like NLTK, Gensim, or scikit-learn to extract insights from unstructured textual data.

Entity Resolution and Data Insights:

Conduct entity resolution for identifying relationships across business and individual names using analytic techniques and methodologies.

Create and refine data insights to inform regulatory enforcement actions and compliance efforts.

Data Visualization: Develop high-quality data visualizations using platforms or libraries such as Matplotlib, Seaborn, or Plotly to communicate complex patterns and findings effectively.

Collaborate with Investigative Teams: Work closely with Investigative Analysts, Enforcement Support personnel, and FinCEN Program Managers to provide analytical support to cases involving violations of the Bank Secrecy Act (BSA) and 31 C.F.R. Chapter X regulations.

Support Enforcement and Regulatory Investigations:

Assist in assessing illicit activities, including money laundering, terrorist financing, proliferation financing, and similar financial crimes.

Analyze transactional data, such as blockchain payments, correspondent accounts, and other financial systems, to uncover fraudulent behaviors, typologies, and violative activities.

Document Analytical Work: Ensure all analytical methodologies, workflows, and findings are described in a clear, concise, and repeatable manner for review by internal and external stakeholders.

Technical Troubleshooting and Problem-Solving: Address and resolve technical challenges in formatting, processing, and analyzing large-scale datasets to ensure robust and reliable analysis.

Data Querying and Processing: Apply advanced skills in relational databases such as SQL Server, Oracle SQL, PostgreSQL, or Hive to query, structure, and analyze datasets.

Process Optimization: Identify opportunities for workflow automation and efficiency improvements using data transformation tools, coding practices (Python, PySpark, object-oriented programming), and statistical techniques.

Knowledge Support: Maintain expertise in key areas including BSA data analysis, financial systems, and the latest advancements in data science technologies to continuously enhance the quality of insights for enforcement and compliance purposes.

Minimum Requirements

Experience and Education:

A minimum of ten (10) years of experience in:

Cleaning, transforming, analyzing, and interpreting complex data sets.

Developing analytical methods, models, or tools to deliver actionable insights.

Bachelor’s degree in fields such as Data Science, Statistics, Computer Science, Mathematics, Economics, or equivalent.

Active Top Secret clearance

Technical Skills and Tools:

Python programming and related machine learning or analytics libraries.

Machine learning methods, including anomaly detection techniques.

Natural Language Processing (NLP) techniques and Python libraries, such as NLTK, Gensim, or scikit-learn.

Data visualization libraries such as Matplotlib, Seaborn, or Plotly to present findings effectively.

Relational databases, such as SQL Server, Oracle SQL, PostgreSQL, or Hive, and advanced querying skills.

Proficiency with Python, PySpark, object-oriented programming, and best coding practices.

Entity resolution for identifying relationships between businesses, individuals, and company names using analytical techniques.

Analyzing and querying large-scale datasets to identify trends, typologies, or actionable results.

Core Competencies:

Strong problem-solving, technical troubleshooting, and communication skills.

Ability to document and present analytical work clearly, concisely, and repeatably.

Preferred Qualifications

Master’s degree in fields such as Data Science, Statistics, Computer Science, Mathematics, Economics, or equivalent.

Professional certifications or training in data science tools, machine learning, financial crime analysis, or related fields.

Experience supporting enforcement and regulatory investigations within Government, law enforcement, or the financial sector.

Familiarity with investigative tools, such as:

BSA Search, FinLab, Transaction Grid Search, or Classified Cloud BSA.

i2 Analyst Notebook or similar advanced analytics and investigations software.

In-depth knowledge of financial industry products and services, including transactional systems like blockchain payments, correspondent banking systems, Fedwire, and Clearinghouse data.

Knowledge of data engineering practices, such as designing data pipelines using PySpark or related tools.

Experience applying machine learning models or natural language processing to illicit financial activity analysis, such as detecting patterns, typologies, or anomalies indicative of BSA violations or related financial misconduct.

Compensation Details:

Anticipated Range $150,000 - $175,000 Annually

The compensation range or hourly rate listed for this position is provided as a good-faith estimate of what the company intends to offer for this role at the time this posting was issued. Actual compensation may vary based on factors such as job responsibilities, education, experience, skills, internal equity, market data, applicable collective bargaining agreements, and relevant laws.

Benefits Overview:

Our health and welfare benefits are designed to support you and your priorities. Offerings include:

Health, dental, and vision insurance

Paid time off and holidays

Retirement benefits (including 401(k) matching)

Educational reimbursement

Parental leave

Employee stock purchase plan

Tax-saving options

Disability and life insurance

Pet insurance

Note: Benefits may vary based on employment type, location, and applicable agreements. Positions governed by a Collective Bargaining Agreement (CBA), the McNamara-O'Hara Service Contract Act (SCA), or other employment contracts may include different provisions/benefits.

Original Posting:

07/24/2026 - Until Filled

Amentum anticipates this job requisition will remain open for at least three days, with a closing date no earlier than three days after the original posting. This timeline may change based on business needs.