Lead Data Analyst​/Data Scientist – Differential Privacy

E-Logic, Inc. is seeking a Lead Data Analyst/Data Scientist specializing in Differential Privacy to support the IRS RAAS Statistics of Income (SOI) Division in developing, evaluating, implementing, and optimizing privacy-preserving methodologies for Qualified Opportunity Zone (QOZ) statistics.

The position will lead the analytical and statistical aspects of the project, including differential privacy methodology, statistical disclosure limitation, privacy-utility analysis, synthetic data development, statistical modeling, evaluation of privacy parameters, protected statistical outputs, technical documentation, stakeholder support, training, and knowledge transfer.

Remote - United States

Employment Type: Part-Time Contract Assignment

Estimated Level of Effort: 320 hours per contract year, approximately 6.2 hours per week on average.

The actual distribution of hours may vary based on project activities, technical requirements, meetings, and deliverables.

• Develop, test, implement, and evaluate statistically valid methodologies for producing aggregated QOZ statistics while protecting taxpayer information.
• Apply statistical disclosure limitation methodologies based on differentially private algorithms implemented in Python.
• Evaluate and apply differential privacy techniques for statistical disclosure control, privacy‑preserving computation, and secure aggregation.
• Evaluate and pilot the Tumult Analytics Python framework or a comparable privacy-preserving analytics framework.
• Develop and refine privacy mechanisms and parameters while balancing privacy protection and statistical utility.
• Evaluate privacy budgets, including epsilon‑level scenarios and the impact of reporting granularity on error and bias.

• Analyze IRS tax and administrative datasets associated with Opportunity Zone investments and Qualified Opportunity Funds.
• Support analysis involving relevant Form 8996 and Form 8997 records.
• Conduct statistical analyses of QOZ and QOF data.
• Support production of aggregated statistics for Government analysis and public reporting.
• Develop and assess methods for releasing datasets such as QOF counts, QOF investment amounts, and percentages of QOZ‑designated census tracts while maintaining FTI confidentiality.

• Develop synthetic test data reflecting the structure and statistical characteristics of underlying population data.
• Develop an evaluation application capable of estimating error rates and bias metrics across different epsilon levels and reporting granularities.
• Quantify privacy‑utility trade‑offs and provide decision‑support information for selecting appropriate privacy parameters.
• Validate statistical outputs and analytical results.
• Generate preliminary visualizations and summary tables demonstrating the feasibility of compliant QOZ statistical outputs.

• Develop a modular, differentially private analytical application that can be reused and adapted for future IRS and Treasury statistical reporting projects.
• Support implementation of the Year 1 statistical modeling framework using TY 2026 data in Year 2.
• Support production‑level analytical pipelines and automated statistical output generation.
• Review and validate methodologies, models, privacy parameters, and analytical outputs.
• Refine statistical methodologies based on stakeholder feedback, data characteristics, and Treasury guidance updates.

• Produce transparent, reproducible analytical code and documentation consistent with Federal statistical standards.
• Contribute to data exploration reports, data dictionaries, technical assessments, methodology…