OverviewJoin our team as a Principal Scientist, Differential Privacy and support the National Institute of Standards and Technology (NIST) Privacy Engineering Group by leading research into privacy-enhancing technologies, including privacy-preserving record linkage, differentially private data access primitives, and the evaluation of de-identified and synthetic data. You will direct the technical work across all task areas, author public-facing reports that formalize trust models and solutions, define rigorous privacy and utility metrics, and serve as the primary technical liaison with NIST staff — ensuring the Testbed delivers reproducible, real-world guidance that advances how sensitive data is shared and protected.
**Position is contingent upon contract award**
Work Schedule and Location:
Remote: This full-time remote position will work Monday through Friday, 8am to 5pm.
Responsibilities
- Provide overall technical leadership across all task areas: PPRL archetype datasets and demonstration solutions, DPDAP descriptions and demonstrations, expansion and analysis of the Collaborative Research Cycle (CRC), and de-identification evaluation methods.
- Lead the design and authoring of public-facing reports that formalize PPRL archetypes, trust models, candidate solutions, and limitations — including glossaries, taxonomies, flowcharts, and decision matrices.
- Define formal metrics for the utility, fidelity, and empirical privacy evaluation of de-identified data.
- Direct the configuration of open-source software to solve PPRL model problems on non-sensitive datasets.
- Recruit and coordinate subject-matter expert (SME) review panels and incorporate their feedback into deliverables.
- Represent the technical work at conferences and workshops (without representing NIST).
Qualifications
Required:
- Ph.D. in computer science with a focus on differential privacy or a related field from an accredited institution (or a demonstrated equivalent combination of education, training, and experience).
- Minimum 10 years' experience in privacy-preserving algorithms.
- Strong applied mathematics background, with proficiency developing efficient algorithms for computationally difficult optimization problems.
- Experience with successful public technology benchmarking exercises.
- Experience successfully deploying demonstration solutions for privacy-preserving data analysis and data sharing.
- Track record as a principal contributor to the development of differential privacy / privacy-preserving algorithms.
- Record of noteworthy publications, invited talks, and contributions.
At FWI, we place the highest importance on creating an exceptional employee experience. You'll have opportunities to achieve your career aspirations through internal promotions, professional development, and other recognition and rewards programs. Join our team and take advantage of the many benefits we offer, including:
- Health Insurance
- Dental Insurance
- Vision Insurance
- Long-term and Short-term Disability Insurance
- Life Insurance
- 401(k) Plan
- Holiday Pay
- Paid Time Off
Pay Range
Negotiable
