Data Scientist (Privacy Engineering & Applied Research)

Roles & Responsibilities

Overview

Our Data Privacy Practice advances national capabilities in privacy-enhancing technologies (PETs) and responsible AI adoption. We combine applied research, engineering, and partnership with public-sector organizations to ensure data can be used safely and effectively for public benefit.

The team’s portfolio includes Advanced Anonymisation, Synthetic Data Generation, Homomorphic Encryption, Differential Privacy, Federated Analytics, Federated Learning, and Trusted Execution Environments . We aim to bridge research and implementation, translating complex PETs into practical, deployable solutions.

Key Responsibilities

Applied Research & Development

Design and execute experiments to evaluate emerging PETs.

Build proof-of-concepts demonstrating real-world applicability and measurable impact.

Stay current with academic research and translate insights into production-ready solutions.

Contribute to knowledge-sharing efforts through technical documentation and publications.

Partnership & Implementation

Collaborate with stakeholders to understand privacy challenges and operational constraints.

Lead pilot programs to validate PET adoption and gather user feedback.

Provide ongoing technical consultation and support through deployment stages.

Solution Design & Innovation

Identify opportunities where PETs can solve cross-agency data challenges.

Architect scalable, interoperable privacy solutions for large-scale use.

Define frameworks and best practices for sustainable technology adoption.

Cross-Functional Collaboration

Work with data scientists, engineers, and product managers to align research and implementation.

Ensure solutions comply with evolving data governance and regulatory requirements.

Requirements

• Bachelor’s degree or higher in Computer Science, Data Science, or related discipline.
• Minimum 2–3 years of hands-on experience in data science, ML, or applied research.
• Strong programming skills in Python and frameworks such as PyTorch, TensorFlow, or scikit-learn.
• Demonstrated experience designing experiments and analyzing model performance.
• Proven ability to interpret and apply academic research to practical solutions.

Preferred Skills

Tell employers what skills you have

TensorFlow

Technical Documentation

Applied Research

Architect

Translating

Pets

Data Governance

PyTorch

Python

Publications

Data Science

Regulatory Requirements

Encryption

Bridge