Data Scientist (DP)

You will be involved in:

1. Applied Research & Development

a. Design and execute rigorous experiments to evaluate emerging PETs and
solutions
b. Develop proof-of-concepts that demonstrate real-world applicability and
measurable impact
c. Stay current with academic research and translate findings into practical,
deployable solutions
d. Contribute to the broader community through publications and knowledge sharing with government and international partner

2. Agency Partnership & Pilot Implementation
a. Collaborate directly with government agencies to deeply understand their unique privacy challenges and operational constraints
b. Design and execute pilot programmes to test PET solutions
c. Systematically gather user feedback and iterate on solutions based on practical deployment experiences and lessons learned
d. Provide ongoing technical consultation and hands-on support to agencies
throughout their privacy technology adoption journey

3. End-to-End Solution Innovation & Development
a. Identify opportunities where PETs can address current common data challenges across government
b. Architect and develop scalable solutions designed for widespread adoption
across the Whole of Government ecosystem
c. Establish clear implementation pathways and adoption frameworks for new
privacy technologies

4. Cross-Functional Collaboration
a. Work as a collaborative team player alongside data scientists, data engineers, software engineers, and product managers
b. Partner strategically with government agencies to align technical solutions with the evolving landscape of regulatory requirements and governance frameworks

Requirements:

1. Bachelors degree or higher in Computer Science, Data Science, Business Analytics or a related field, with at least 2-3 years of relevant professional experience.
2. Core Data Science & ML skillset

• Strong foundation in machine learning, with hands-on experience in model development and experimentation.
• Strong programming proficiency in Python and extensive experience with ML frameworks (e.g., PyTorch, TensorFlow, scikit-learn).
• Ability to analyze model behavior, diagnose training issues, and design
  experiments to optimise performance and reliability.

3. Applied Research & Experimentation

• Demonstrated ability to read, synthesise, and critically evaluate academic research papers and technical literature
• Experience designing and conducting rigorous experiments to validate hypotheses and measure solution effectiveness
• Comfort working with ambiguous problems and developing novel approaches to complex challenges.
  

4. Nice-to-Haves

• Experience with privacy-enhancing technologies, including but not limited to anonymisation, synthetic data generation or differential privacy.
• Familiarity with frontend integration workflows (Next.js/React).Prior experience working in multi-disciplinary teams.

5. Mindset & Collaboration

• Curiosity and willingness to learn new domains (esp. data privacy).

• Strong communication skills to explain technical concepts to both engineers and non-technical stakeholders.
• Inclination to work in a collaborative, fast-moving Agile environment.
• Strong in communication with the ability to explain complex technical concepts clearly to diverse audience.