Data Scientist (with Software Engineering Competency)

Roles & Responsibilities

Overview

Our Data Privacy & Engineering team develops advanced tools and frameworks that strengthen data protection and enable privacy-preserving innovation across the public sector. We build solutions such as anonymisation and synthetic data generation systems that empower organizations to safely leverage data for analytics and AI applications.

We are seeking a Data Scientist (with Software Engineering Competency) to join our Synthetic Data Generation (SDG) product team. This role sits at the intersection of data science, applied machine learning, and software engineering , supporting the continued development of a national-scale synthetic data platform currently serving multiple agencies.

Key Responsibilities

Model Development

Design and evaluate emerging SDG models (e.g., DDPM, ARF, Gaussian Copula).

Investigate and resolve model limitations related to data types, sizes, and cardinality.

Optimize architectures, tune hyperparameters, and propose novel modeling strategies.

Feature & Product Development

Collaborate with software engineers to implement ML-driven product features.

Develop algorithms for imputation, constraint handling, and privacy metrics.

Diagnostics & Debugging

Troubleshoot user issues related to model performance and integration.

Provide actionable technical insights and communicate findings clearly.

Documentation & Knowledge Sharing

Produce clear, user-facing documentation on model design, usage, and metrics.

Translate complex ML concepts into accessible language for end users.

Cross-Functional Collaboration

Work closely with the engineering team (Next.js, FastAPI, AWS) to integrate models into production systems.

Participate in Agile rituals, design discussions, and code reviews.

Requirements

• Bachelor’s degree or higher in Computer Science, Data Science, or a related field.
• 2–3 years’ professional experience in applied ML or data science.
• Strong Python proficiency with experience in frameworks such as PyTorch, TensorFlow, or scikit-learn.
• Familiarity with backend development (FastAPI, Flask) and cloud environments (AWS preferred).
• Working knowledge of Git, CI / CD, and collaborative coding best practices.
• Excellent analytical and problem-solving skills.

Nice-to-Haves

Mindset

Tell employers what skills you have

TensorFlow

Machine Learning

Git

Modeling

Public Sector

Software Engineering

Agile

PyTorch

Flask

Python

Data Science

Product Development

Debugging

Bridge

FastAPI