Senior Developer, Privacy Enhancing Technology

Location: Hybrid, National Capital Region (onsite required 4 days per month)
Type: Contract, 12 months
Hours: Full time, 37.5 hours per week
Clearance: Eligible for Government of Canada Secret clearance required
Language: English

Our client, a federal regulatory organization, is seeking a Senior Developer with deep expertise in privacy enhancing technologies to join their data science team. This is a hands on technical role building and validating PET solutions for structured and unstructured data in a regulated environment.

Responsibilities

Develop and implement privacy enhancing technologies for structured and unstructured datasets in collaboration with subject matter experts
Apply core computing principles to integrate open source libraries, components and platforms into cohesive, operational solutions
Design, develop and validate proof of concept solutions with a focus on production deployment
Build test harnesses to evaluate and compare solution approaches against performance, scalability, quality and privacy metrics
Create, maintain and optimize ETL pipelines to ingest, transform, validate, sanitize and prepare data for analysis
Provision, configure, orchestrate and manage cloud resources in support of deployed solutions
Assess and recommend emerging tools, technologies and processes to improve solution performance, efficiency, scalability and maintainability
Analyze results and technical findings, develop recommendations and present conclusions to stakeholders
Prepare technical reports, data artifacts, presentations and documentation to support project execution and decision making

Qualifications

Master's or PhD in a field related to privacy enhancing technologies, artificial intelligence, machine learning, cybersecurity, cryptography, computer science, data science, mathematics or statistics
Minimum 5 years of recent Python development experience with numpy, scipy, pandas, polars, pyspark, pytorch, scikit learn and matplotlib or seaborn
Minimum 3 years of recent experience with DevOps principles and practices, including SOLID principles, version control, and continuous integration and deployment for data science applications
Experience analyzing and working with large, complex datasets within public sector, financial services or similarly regulated environments
Experience developing and implementing PETs, including data anonymization, de identification, differential privacy, secure multi party computation, federated learning, homomorphic encryption or similar techniques
Knowledge of data structures, models and relationships across structured, semi structured, unstructured, nested, graph and network based datasets
Experience with cloud based data and analytics platforms, including Microsoft Azure, Microsoft Fabric, Azure Data Lake, Azure Databricks, Jupyter Notebooks and Docker
Knowledge of Linux computing environments and basic scripting
Knowledge of privacy preserving and statistical techniques such as Monte Carlo simulation, Markov models, synthetic data generation, differential privacy, homomorphic encryption and zero knowledge proofs
Knowledge of machine learning concepts including GANs, transformers, auto encoders, RAG and model development methodologies
Knowledge of infrastructure automation concepts, including Ansible, Terraform, Puppet, Chef or equivalent

Candidates must be based in Canada and legally authorized to work in Canada.