About Mind Moves
Mind Moves is a women-owned Washington, D.C.-based firm helping businesses and governments to embrace, build and deploy ‘human in the loop’ AI solutions to elevate mission and value. We are pioneers with deep expertise in the field of machine learning and AI (both predictive and generative). We have delivered responsibly developed products and services that impact millions of users worldwide, while generating millions of dollars in revenue. We have worked with Fortune 500 corporations and large government entities in health care, supply chain management, financial services and life sciences. We apply a structured methodology and holistic approach to AI transformation and a broad spectrum of technical services centered in the harnessing of people, science and technology.
What you’ll be doing
Designing and building synthetic data generation pipelines that produce high-fidelity, statistically representative datasets modeled on sensitive, controlled-access data sources — without exposing real participant-level information. Your synthetic datasets will let cross-functional teams prototype, test, and red-team AI/agentic systems against realistic data conditions before those systems ever touch production, controlled-access repositories.
Working with Mind Moves
You’ll collaborate with the Principal Investigator/Technical Lead, Biomedical Privacy & Gateway Engineer, Red-Team/Adversarial Security Lead, Cybersecurity Engineer, and the broader Mind Moves project team — while working closely with institutional stakeholders and Data Access Committees at NIH. This role offers the chance to:
Make a measurable impact on how safely AI systems handling sensitive genomic and health data can be developed and tested.
Gain hands-on experience with privacy-preserving data generation for frontier AI/ML pipelines.
Operate at the intersection of data engineering, privacy, and federal data governance in a creative, mission-driven environment.
The contract will include:
Design and build synthetic data generation pipelines (statistical modeling, generative models, or agent-based simulation) that replicate the structure and statistical properties of controlled-access datasets
Generate realistic synthetic test/dev datasets (e.g., genomic sequence-style data modeled on dbGaP) to support prototyping, MVP development, and red-team testing without exposing real participant-level data
Apply and tune privacy-preserving techniques, including differential privacy noise injection, to bound re-identification and membership-inference risk
Validate synthetic data fidelity — statistical distribution matching, utility-preservation testing, and downstream model performance parity against real data benchmarks
Assess and document residual privacy risk (e.g., re-identification, linkage, membership inference) for each synthetic dataset release
Collaborate with the Red-Team/Adversarial Security Lead and Biomedical Privacy & Gateway Engineer to supply adversarial and edge-case synthetic scenarios for testing
Contribute methodology documentation and data provenance records for review by institutional Data Access Committees (DACs)
Collaborate with cross-functional teams across federal agencies and supporting partners
Contractor has the following skills/background:
Demonstrated experience building synthetic data generation pipelines using statistical modeling, generative models (e.g., GANs, VAEs, diffusion), or agent-based simulation
Experience engineering and transforming large-scale genomic formats (e.g., multi-sample VCFs, PLINK binary sets, Hail MatrixTables) alongside complex, longitudinal clinical and phenotypic data.
Advanced proficiency in Python and SQL, with hands-on experience using synthetic data, privacy, and scientific libraries (e.g., Opacus, Diffprivlib, PyTorch, Hail, Polars, or Spark).
Data engineering and ETL skills — pipeline design, schema modeling, API integration
Knowledge of AI/data governance frameworks such as NIST AI RMF and federal (NIH/HHS) compliance requirements preferred
Background or experience in biomedical, bioinformatics, or life sciences preferred
Experience taking synthetic data pipelines from proof of concept through to production-grade, reusable tooling
Strong communication skills; ability to document methodology and risk assessments for non-technical and institutional stakeholders
U.S. work authorization for 1099 contracting; Mind Moves cannot offer sponsorship for this contract
Proposal Details
Compensation range: $100-130/hr
Estimated contract duration: September - December
Contract hours: 20-25 hours/week
Work locale: Remote in the US (EST time zone preferred)
Read more about Mind Moves: https://mindmoves.co/
Timeline and Process
Proposals are accepted on a rolling basis until the position is filled. The contract bid process will include 3 interviews and the invitation to complete a technical exercise.
Experience building synthetic data pipelines, genomic and clinical data engineering, Python, SQL, ETL, privacy-preserving methods, and scientific libraries; strong documentation and communication skills required.
Key Responsibilities
- building pipelines
- generating datasets
- assessing privacy
Skills & Tools
Python, SQL, GANs, VAEs, Opacus, Diffprivlib, PyTorch, Hail, Polars, Spark, NIST AI RMF, VCF, PLINK, Hail MatrixTables
Job Details
- Category: Data and Analytics
- Seniority: Senior Level
- Commitment: Contract
- Workplace: Remote — United States
- Salary: USD 208,000 – 270,400 / year
- Languages: English
About Mind Moves
A women-owned consulting firm helping businesses and governments build and deploy human-centered artificial intelligence solutions. — Industry: Consulting Services