Data Scientist

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Priority may be given to the following designated employment equity groups: women, Indigenous Peoples* (First Nations, Inuit and Métis), persons with disabilities and racialized persons*.

* The Employment Equity Act, which is under review, uses the terminology Aboriginal peoples and visible minorities.

Candidates are asked to self-declare when applying to this hiring process.

OrganizationalUnit: Digital Technologies

Classification: RCO

Duration:

2 years

Language Requirements: English or French

Work arrangements:

Due to the nature of the work and operational requirements, this position may be eligible for a limited hybrid work arrangement (combination of working onsite and telework).

At the NRC, we recognize that Indigenous candidates may have important connections to their communities and you may be eligible for an exception to this work arrangement. Alternative work arrangements may also be considered to accommodate candidates as required. To learn more about these options, please contact the NRC Hiring team using the contact information below.

The role Key Responsibilities Client Engagement and Project Scoping

Meet with Canadian SMEs to understand their business context, operational challenges, data assets, and decision‑making needs.

Help clients clarify and refine business problems into feasible data science, analytics, or AI projects.

Contribute to project scoping, including technical approaches, work plans, timelines, assumptions, risks, and deliverables.

Communicate clearly with clients throughout the project lifecycle, including managing expectations and explaining analytical findings.

Applied Data Science and Statistical Modelling

Acquire, clean, process, explore, and analyze structured and unstructured data.

Develop statistical, machine learning, and analytical models to address client problems.

Apply appropriate methods such as regression, classification, clustering, forecasting, optimization, natural language processing, simulation, or other relevant techniques.

Evaluate model performance, uncertainty, limitations, and practical usefulness.

Translate analytical results into actionable recommendations for clients.

Coding, Reproducibility and Delivery

Write clear, efficient, maintainable code in Python, R, or similar languages.

Develop reproducible analytical workflows using notebooks, scripts, version control, and good documentation practices.

Build prototypes, dashboards, data pipelines, reports, and reusable analytical tools.

Deliver client‑ready code, models, documentation, and analytical outputs.

Balance rigour with practical delivery timelines in a project‑based environment.

Visualization and Communication

Create clear visualizations, dashboards, and reports that communicate insights effectively.

Present technical findings to both technical and non‑technical audiences.

Prepare concise written deliverables, including summaries, reports, presentations, and recommendations in plain language.

Teamwork and Continuous Learning

Work collaboratively in small teams of data scientists, software developers, data engineers, researchers, and business development staff.

Manage multiple priorities and deadlines across client‑facing projects.

Stay current with emerging data science, machine learning, and AI practices.

Contribute to NRC practices for responsible, transparent, and reproducible AI.

Screening criteria Applicants must demonstrate within the content of their application that they meet the following screening criteria in order to be given further consideration as candidates:

Education

A master’s degree in data science, statistics, computer science, mathematics, engineering, operations research, economics, or another relevant field; or

A bachelor’s degree in a relevant field combined with significant experience in applied data science, statistics, analytics, machine learning, or related work may be considered.

Assets

Experience with cloud platforms such as Azure, AWS, or GCP.

Experience in in modern visualization frameworks and interactive dashboards.

Experience with MLOps, containerization, CI/CD, model deployment, or production analytics workflows.

Experience with natural language processing, generative AI, computer vision, optimization, forecasting, or simulation.

Applying data science, statistical analysis, machine learning, or advanced analytics to solve real‑world problems.

Working with clients, stakeholders, or end users to understand needs, gather requirements, and define analytical approaches.

Cleaning, process