Data Scientist

In this role, you will apply data science and machine learning to analyze complex financial transaction data for a major federal intelligence and law enforcement bureau. Your analytical models will help identify suspicious patterns, supporting efforts to safeguard the financial system from illicit activity and money laundering.
This work would be performed on-site in Washington, DC.

• Financial Crime Pattern Detection: Designing, developing, and deploying machine learning models and statistical algorithms to identify complex money laundering techniques—such as structuring, layering, and smurfing—within massive financial datasets.
• Cloud-Native Data Engineering & Analysis: Performing exploratory data analysis, feature engineering, and model validation using Python, PySpark, and SQL across scalable AWS infrastructure, including S3, RDS, and Open Search.
• Stakeholder Collaboration & Translation: Partnering directly with compliance analysts and federal investigators to translate complex regulatory requirements into high-impact analytical models and clear visual reports.
• Model Integrity & Workflow Standardization: Building maintainable data pipelines, document model logic to agency standards, and lead peer code reviews to ensure reproducible, high-quality data science practices.

• Active Top Secret SCI clearance
• Bachelor’s Degree or higher in a related field from an accredited college or university
• 4+ years of dedicated data science experience building, validating, and deploying machine learning models using Python and/or R.
• Hands-on experience analyzing Bank Secrecy Act (BSA) or Anti-Money Laundering (AML) transaction data to detect illicit financial patterns.
• Hands-on experience executing queries and managing data pipelines within AWS cloud environments (e.g., S3, RDS/PostgreSQL, Open Search, Lambda).
• Strong proficiency with SQL and big-data frameworks (e.g., PySpark, Pandas) to analyze large-scale structured and unstructured datasets.

• Experience building intuitive data dashboards and clear visual reports to present findings to non-technical operational teams.
• Demonstrated track record of establishing automated model documentation and reproducible data pipelines in a regulated environment.

At Ascella, we celebrate innovative thinkers who are empowered to share their ideas and tackle real business challenges. Your creativity will not only be welcomed but also essential in shaping our processes and solutions.

We are passionate about lifelong learning
, fostering a culture that prioritizes professional development and personal growth. Here, you’ll collaborate with a talented team that supports and inspires each other, ensuring that you’re continually challenged and encouraged to expand your skills.

Client satisfaction is at the heart of our mission. Our employees are dedicated to providing exceptional service, proactively addressing both current and future client needs. You’ll be part of a team that values the client experience as a key component of our success.

Open communication is vital to our work environment. We maintain an open door policy, encouraging questions and the sharing of ideas. We believe in aligning individual contributions with company objectives, fostering transparency, and regularly seeking feedback to enhance our processes.

Lastly, we are committed to being an inclusive organization. We embrace diversity, ensuring that all employees feel valued and respected. Our varied talents and experiences drive creativity and innovation, making our workplace dynamic and successful.

*******************************************************

Position: Data Scientist
Location: Washington, DC (on-site)
Base Compensation: $160-190K
* Other Compensation: 4% contribution to 401(k), performance bonus, profit-sharing
Paid Time Off: 16 days PTO/year, 11 holidays/year
Benefits: Medical, Dental, Vision, Life, Disability, 401(k)…and more
Position is open until filled, applications accepted on an ongoing basis.

** Note on Compensation:
This range represents the good-faith estimate for this position at the time of…