Data Scientist

Overview

As a Data Scientist, you will support the Internal Revenue Service’s mission to improve tax compliance, fraud detection, and risk identification across large, complex tax and financial data environments. You will work directly with government clients, program managers, and technical teams to understand business and compliance challenges, design analytical approaches, and deliver data-driven solutions that inform enforcement, audit prioritization, and fraud prevention efforts.

In this role, you will develop, test, and deploy predictive and statistical models using structured and unstructured data to identify anomalies, non-compliance risk, and potential fraud within tax records and related datasets. You will contribute across the full data science lifecycle, from problem formulation and data exploration through model validation, deployment, and stakeholder communication.

• Prior programming experience, preferably in Python or R, including data exploration, feature engineering, and model development
• Explore, clean, and wrangle large, complex datasets to uncover insights and identify opportunities for data science–driven solutions in support assessments, gap analyses, and actionable recommendations for IRS stakeholders
• Design, develop, test, and implement quantitative and qualitative data science solutions that are modular, maintainable, and adaptable to evolving government and regulatory requirements
• Demonstrated experience using Python, SQL, and Databricks for data analysis, modeling, and statistical evaluation
• Apply statistical and machine learning techniques to anomaly detection, fraud identification, and non-compliance risk scoring, including supervised and unsupervised approaches to help prioritize cases based on compliance risk, fraud indicators and business impact
• Develop and evaluate predictive risk models used to support audit selection, refund review, and fraud prevention initiatives
• Test and validate models using robustness, sensitivity, and significance testing, ensuring defensible and explainable results
• Write modular, reusable, and well-documented code within an iterative development process that includes peer review and collaboration
• Collaborate with clients, subject matter experts, and cross-functional teams to refine problem statements, requirements, and analytical approaches
• Prepare and deliver technical and non-technical briefings, reports, and presentations to audiences with varying levels of analytical sophistication
• Demonstrated ability to work independently in a collaborative, fast-paced environment
• Strong interpersonal, written, and verbal communication skills, with the ability to translate business and compliance needs into technical solutions
• Experience using Markdown for technical documentation

• Bachelor of Science degree in a relevant field such as statistics, computer science, economics, mathematics, analytics, data science, business, or social sciences
• 2-10+ years of experience in data science, analytics, or a related technical field
• Experience using version control systems (e.g., Git) and collaborative development practices
• Strong understanding of relational databases and SQL
• Comfortable learning new tools, methodologies, and domains, including working outside your comfort zone
• Strong analytical mindset with a willingness to tackle complex mathematical and statistical challenges
• Willingness to travel and work on-site at client locations as required by project needs

• Advanced degree (MS or PhD) in statistics, computer science, data science, mathematics, analytics, engineering, or related fields
• Experience with PySpark, Unity Catalog, and Jobs in Data Bricks
• Experience with Natural Language Processing (NLP) and text analytics applied to unstructured documents or case notes
• Experience with graph analytics and network analysis to identify relationships, fraud rings, or interconnected entities
• Familiarity with platforms and tools such as Databricks, and AWS
• Experience with containerization and environment management (e.g., venv, conda)
• Understanding of the data analytics lifecycle (e.g., CRISP-DM)
• Experience applying advanced…