Job description

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. Working Hours 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. Key Responsibilities Differential Privacy & Statistical Disclosure Limitation • 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. QOZ Data Analysis • 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. Synthetic Data & Evaluation • 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. Analytical Application & Production Implementation • 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. Documentation & Technical Reporting • Produce transparent, reproducible analytical code and documentation consistent with Federal statistical standards. • Contribute to data exploration reports, data dictionaries, technical assessments, methodology documentation, QA documentation, and technical reports. • Document analytical methods, assumptions, privacy mechanisms, testing procedures, results, limitations, and privacy-utility trade-offs. • Support preparation of documentation suitable for internal review, governance processes, OMB submission, and public release, as applicable. Training & Knowledge Transfer • Provide structured training, workshops, code walkthroughs, and one-on-one mentoring for SOI analysts and data scientists. • Develop user guides, technical documentation, troubleshooting procedures, instructional materials, and recorded training sessions. • Support the transition of contractor-developed capabilities to fully self-sustaining Government operations. • Communicate technical aspects of differential privacy to internal and external stakeholders. Qualifications • Proven experience utilizing differential privacy Python libraries such as Tumult Analytics or comparable privacy-preserving data analysis frameworks. • Demonstrated experience with statistical disclosure control, differential privacy implementation, and secure data aggregation. • Prior experience working with Federal datasets and applicable confidentiality, security, and governance requirements. • Familiarity with Privacy Act, CIPSEA, FISMA, OMB guidance, and Federal statistical data practices. • Experience integrating privacy-preserving analytics into large-scale data workflows. • Experience developing analytic tools, pipelines, dashboards, or applications using privacy-preserving computation is highly desirable. • Experience producing transparent and reproducible codebases. • Strong statistical analysis, analytical modeling, and technical problem-solving capabilities. • Demonstrated experience developing technical documentation and training materials. • Experience supporting knowledge transfer from contractor-developed capabilities to Government staff. • Ability to work collaboratively with statistical, data engineering, policy, and IT stakeholders. Federal Project Requirements • U.S. citizenship is required for this federal contract. • Must be able to obtain and maintain the required federal clearance/security approval before receiving GFI. • Must be eligible to receive and use contractor PIV card, as applicable. • Must work only within authorized client systems and approved computing environments when handling Government data. • Must protect FTI, taxpayer information, and other sensitive Government information. • Must follow applicable Federal and IRS confidentiality, security, and data-governance requirements. • AI-assisted tools may be used as part of project execution; all AI-assisted analytical, coding, testing, documentation, and reporting outputs must undergo human review, validation, and approval before use. Work Environment & Physical Requirements • Fully remote position within the United States. • Work is performed primarily at a computer using Government-approved systems and collaboration tools. • Ability to participate in virtual meetings, technical discussions, reviews, and training sessions. • Ability to analyze large datasets and maintain detailed technical documentation. • No routine physical labor is expected. EEO Employer Statement E-Logic, Inc. is an Equal Employment Opportunity Employer and considers all qualified applicants for employment without regard to race, color, religion, sex, national origin, age, disability, veteran status, or any other status protected by applicable federal, state, or local law.

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Working Hours
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.
Differential Privacy & Statistical Disclosure Limitation
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.
QOZ Data Analysis
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.
Synthetic Data & Evaluation
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.

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pythonai

Platform: discovered
Effort: medium

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